Analysis of old and young neural stem cells sequenced with the SmartSeq2 protocol
Load data for young and old neural stem cells sequenced with the Smart-Seq2 protocol
NSCs_annotation <- read.csv(file = "count_table/NSCs_annotation.csv" , row.names = 1)
TPM_NSCs <- read.csv(file = "count_table/TPM_NSCs.csv" , row.names = 1)Make the cell names also rownames
rownames(NSCs_annotation) <- NSCs_annotation$cell
head( NSCs_annotation )Load the R packages needed for the analysis
We need the Matrix package to use sparse matrices to save memory while computing. The ggplot2 package is used for plotting. Seurat is used to create PCA and tSNE plots.
require(Matrix)Loading required package: Matrixrequire(tidyverse)Loading required package: tidyverse
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[31m✖[30m [34mdplyr[30m::[32mlag()[30m masks [34mstats[30m::lag()[39mrequire(ggplot2)
require(gridExtra)Loading required package: gridExtra
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combinerequire(tidyverse)
require(clusterProfiler)Loading required package: clusterProfiler
Loading required package: DOSE
DOSE v3.4.0 For help: https://guangchuangyu.github.io/DOSE
If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609
clusterProfiler v3.4.4 For help: https://guangchuangyu.github.io/clusterProfiler
If you use clusterProfiler in published research, please cite:
Guangchuang Yu., Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Attaching package: ‘clusterProfiler’
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simplifyrequire(Seurat)Loading required package: Seurat
Loading required package: cowplot
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ggsaveset.seed(123)Load the monocle pseudotime results
pseudotime_ordering <- read.csv(file = "Monocle/pseudotime_ordering.csv" , row.names = 1 )Check innate immune response and interferon response GO category genes
Create a Seurat object using the TPM values for the SmartSeq2 data and use the same normalization strategy as for the 10X sequencing run.
Use the TPM data.frame to initialize a Seurat object
TPM_NSC <- remove_rownames(TPM_NSCs) %>% column_to_rownames( var = "ensembl_gene_id")
seurat_sms2 <- CreateSeuratObject(raw.data = TPM_NSC , min.cells = 3, project = "young_vs_old_SmartSeq2")Set identity to NSC to begin with.
seurat_sms2 <- SetIdent(object = seurat_sms2 , ident.use = "NSC")VlnPlot(object = seurat_sms2, features.plot = c("nGene", "nUMI"))
Add metadata: age of the animal, cell name and celltype (activation state)
seurat_sms2 <- AddMetaData(object = seurat_sms2, metadata = NSCs_annotation )Log-normalize the TPM data
seurat_sms2 <- NormalizeData(object = seurat_sms2, normalization.method = "LogNormalize")Performing log-normalization
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**************************************************|seurat_sms2 <- ScaleData(object = seurat_sms2, vars.to.regress = c("nUMI", "nGene"))Regress out difference between S phase and G2/M Phase
First we load the genes related to the cell cycle phases G2/M and S and make them matching to the gene symbols in the dataset by converting to ENSEMBL_IDs .
s.genes <- bitr(geneID = stringr::str_to_title(cc.genes$s.genes) , fromType = "SYMBOL" , toType = "ENSEMBL" ,drop = FALSE ,OrgDb = "org.Mm.eg.db")Loading required package: org.Mm.eg.db
Loading required package: AnnotationDbi
Loading required package: stats4
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select
'select()' returned 1:1 mapping between keys and columns
2.33% of input gene IDs are fail to map...g2m.genes <- bitr(geneID = stringr::str_to_title(cc.genes$g2m.genes) , fromType = "SYMBOL" , toType = "ENSEMBL" ,drop = FALSE ,OrgDb = "org.Mm.eg.db")'select()' returned 1:many mapping between keys and columns
3.7% of input gene IDs are fail to map...seurat_sms2 <- CellCycleScoring(object = seurat_sms2, s.genes = s.genes$ENSEMBL , g2m.genes = g2m.genes$ENSEMBL , set.ident = FALSE)Then we calculate the difference between the cell cycle scores and regress our data on that difference.
seurat_sms2@meta.data$CC.Difference <- seurat_sms2@meta.data$S.Score - seurat_sms2@meta.data$G2M.Score
seurat_sms2 <- ScaleData(object = seurat_sms2, vars.to.regress = "CC.Difference", display.progress = FALSE)Load a list of genes from the innate immune response GO category
results_interferon_response_immune <- structure(list(mgi_symbol = c("Uba7", "Ube2l6", "Ube2e1", "Ube2e2",
"Isg15", "Ikbke", "Setd2", "A230050P20Rik", "Mx2", "Shmt2", "Irgm2",
"Dnaja3", "Kynu", "Bst2", "Sec61a1", "Cited1", "H2-Aa", "Slc11a1",
"Gch1", "Ubd", "Il23r", "Slc30a8", "Cxcl16", "Cyp27b1", "Trim21",
"Ifitm2", "Ifitm1", "Ifitm3", "Adamts13", "Tgtp2", "Tgtp1", "Snca",
"Cd40", "Mefv", "Ciita", "Klhl20", "Adar", "Ifnar2", "Eif2ak2",
"Plscr1", "Lamp3", "Xaf1", "Stat1", "S100a8", "Trdc", "S100a9",
"Aim2", "Mb21d1", "Irf1", "Pglyrp4", "Sdhaf4", "Ddx3x", "Defb1",
"Serinc5", "Ddx58", "Defb12", "Defb34", "Nlrp10", "Rela", "Gata3",
"Csf1r", "Igha", "Defb15", "Rnase6", "Defb35", "Defb13", "Pglyrp3",
"Ighg2c", "Krt16", "Tril", "Nlrp9c", "Bpifb3", "Pik3ca", "Nlrp1a",
"Nlrp9b", "Ighg3", "Ighd", "Ighm", "Nlrp1b", "Cfi", "Ly96", "Jchain",
"C8b", "Nlrx1", "Cybb", "Pml", "C8a", "Tnk1", "Polr3c", "Il1rap",
"Rbm14", "Polr3e", "Anxa1", "Cfd", "Ifnk", "Fcnb", "Nr1h4", "Defb43",
"Nono", "5730559C18Rik", "Hmgb3", "Defb30", "Sh2d1b2", "Ighv5-2",
"Ighv2-2", "Tnfaip8l2", "Tarm1", "Ighv5-4", "Bpifa1", "Nod2",
"Ighv2-3", "Otulin", "Ighv5-6", "Bpifb1", "Ifi202b", "Ighv5-9",
"Fcgr1", "Trim28", "Arg1", "Trbc1", "Pik3cg", "Defb18", "Defb41",
"Ighv2-5", "Ighv5-12", "Ighv2-6", "Ighv5-9-1", "Polr3g", "Cyld",
"Sh2d1a", "Zap70", "Ifit3", "Rsad2", "Msrb1", "Ifit1", "Cr1l",
"Cfp", "Sp110", "Trbc2", "Casp4", "Nod1", "Sla2", "Smpdl3b",
"Tlr5", "Ighv5-12-4", "Ighv2-9-1", "Axl", "Map3k5", "Cd55", "Blk",
"Myd88", "Tnfsf4", "Ighv2-6-8", "Ighv2-7", "Ighv5-15", "Ighv5-16",
"Ighv5-17", "Ighv2-9", "Irf5", "Ighv7-1", "Ighv7-2", "Ighv14-1",
"Ighv4-1", "Ighv3-1", "Ighv11-1", "Trim11", "Sla", "Ighv14-2",
"Ighv11-2", "Ighv14-3", "Trim14", "Ighv16-1", "Ighv9-1", "Dhx58",
"Trim13", "Pqbp1", "Cyba", "Ighv9-2", "Ighv9-3", "Ighv7-3", "Ankhd1",
"Ighv14-4", "Ighv3-3", "Oasl2", "Ighv7-4", "Ighv3-4", "Nrros",
"Tlr13", "Fes", "Ticam1", "Ighv3-6", "Ighv9-4", "Apobec3", "Ighv3-8",
"Ighv13-2", "Ankrd17", "Trim15", "Ighv12-3", "Ighv6-3", "Oasl1",
"Trim10", "Tlr1", "Ighv6-4", "C4bp", "Trim5", "Tlr6", "Trim31",
"Polr3b", "Mavs", "Vnn1", "Apcs", "Akirin2", "Ssc5d", "Trim12a",
"Polr3d", "Clec5a", "Fgg", "Dab2ip", "Hmgb2", "Fga", "Fgb", "Trim12c",
"Fgr", "Ipo7", "Mst1r", "Trim30b", "Trim30c", "Sfpq", "Il34",
"Trim25", "Trim30a", "Trim30d", "Pspc1", "Card9", "Mbl1", "Ptk2",
"Lcn2", "Abl1", "Sftpd", "Tlr8", "Pycard", "Ighv6-5", "Tlr2",
"Ighv6-6", "Ighv6-7", "Serping1", "Tlr7", "Src", "Ighv8-2", "Iglc1",
"Ighv10-1", "Ighv1-4", "Il1f6", "Iglc4", "Ighv1-5", "Ighv10-3",
"Ighv1-7", "Iglc2", "Defb23", "Gper1", "Cnpy3", "Trdv4", "Defb20",
"Defb22", "Defb26", "Defb28", "Il1f5", "Defb29", "Ighv15-2",
"Ighv1-9", "Defb21", "Itch", "Pglyrp1", "Nfkb1", "Defb19", "Defb45",
"Il1rl2", "Defb36", "Mid2", "Defb25", "Ighv1-11", "Ighv1-12",
"Ighv1-15", "Ighv1-16", "Grb2", "Ighv1-18", "Ighv1-19", "Ighv1-20",
"Map4k2", "Il1f8", "Pik3cb", "Adam15", "Ighv1-22", "Bcl10", "Ighv1-23",
"Ighv1-24", "Ighv1-26", "Prkdc", "Irak4", "F2rl1", "Adgrb1",
"Isg20", "C1qb", "C1qc", "C1qa", "Mr1", "Polr3a", "Tlr9", "Ighv1-76",
"Optn", "Ighv1-77", "Ptx3", "Ighv1-78", "Zbtb1", "Grap", "Lbp",
"Arhgef2", "Mcoln2", "Ighv1-80", "Tbk1", "Ighv1-81", "Ifih1",
"Ighv1-31", "Bmx", "Ighv1-82", "Ighv1-34", "Ly86", "Nlrc5", "Ighv1-85",
"Polr3f", "Ighv1-36", "Ighv1-37", "Tlr11", "Trim27", "Malt1",
"Fadd", "Ighv1-39", "Ighv1-42", "Ighv1-43", "Btk", "Dcst1", "Parp14",
"Ighv1-47", "Ighv8-4", "Ighv1-49", "Itk", "Ighv8-5", "Ighv1-50",
"Trim26", "Chid1", "Csk", "Fcer1g", "Ighv1-52", "Ighv1-53", "Ighv8-6",
"Naip2", "Nlrp3", "C8g", "Ighv1-54", "Csf1", "Traf3", "Ighv1-55",
"Ighv1-56", "Elf4", "Dtx3l", "Irak1", "Naip6", "Parp9", "Ighv8-8",
"Ighv1-58", "Pik3cd", "Hck", "Ighv1-59", "Cd180", "Irf3", "Ighv1-61",
"Ighv1-62-1", "Ifnl2", "Relb", "Ifnl3", "Fyn", "Ighv1-62-2",
"Ighv1-62-3", "Hmgb1", "Tlr12", "Ighv8-9", "Yes1", "Ighv1-63",
"Ighv1-64", "Tbkbp1", "Siglecg", "Ighv8-11", "Ighv1-66", "Cfh",
"Zc3hav1", "Ighv1-67", "Ighv1-69", "Ighv8-12", "Havcr2", "4933415F23Rik",
"Trim32", "Ighv8-13", "C3", "Ighv1-74", "Trim62", "Hexim1", "Matr3",
"Ighv1-75", "Slpi", "Zfp809", "Igll1", "Arid5a", "Tollip", "Tlr4",
"Syk", "C4b", "Cd244", "Fbxo9", "Ly9", "Ifnb1", "Zfp683", "Ifna15",
"Ifna9", "Ifna14", "Ifna12", "Lck", "Serinc3", "Nlrp6", "Camp",
"Ifna13", "Ifna16", "Ifnab", "Lyn", "Gm13271", "Gm13283", "Gm13290",
"Gm13289", "Gm13272", "Cd24a", "Spon2", "Lgr4", "Ifnz", "Gm13276",
"Gm13277", "Gm13278", "Tlr3", "Gm13275", "Gm13279", "Gm13285",
"Gm13287", "Gm13288", "Ifna11", "Ifna4", "Oas2", "Prkd1", "Oas3",
"Slamf1", "Marco", "Ifne", "Rarres2", "Clec4a2", "Treml4", "Oas1a",
"Slamf6", "Fcna", "Sarm1", "Atg5", "Trim59", "Clec4n", "Herc6",
"Ripk2", "Irgm1", "Prdm1", "Hc", "Clec4d", "Clec4e", "Ticam2",
"Trem2", "Il27", "Frk", "Mif", "Ptk2b", "C1rl", "Irf7", "C1ra",
"C1rb", "C1s2", "Polr3k", "Zbp1", "Trim35", "Il23a", "C2", "Cfb",
"Padi4", "Cactin", "Masp2", "Klrg1", "Arg2", "Ptk6", "Clec7a",
"Rnf135", "Txk", "Klrk1", "Sec14l1", "Crcp", "Styk1", "Tmem173",
"Atg12", "Gm17416", "Iigp1", "Polr3h", "Riok3", "Cd14", "Gm5849",
"Mbl2", "Akap8", "Xrcc5", "Nfkb2", "Masp1", "Trim8", "Pcbp2",
"Ppp1r14b", "Gsdmd", "Fer", "Grap2", "Nlrc4", "Map4k2"), external_gene_name = c("Uba7",
"Ube2l6", "Ube2e1", "Ube2e2", "Isg15", "Ikbke", "Setd2", "A230050P20Rik",
"Mx2", "Shmt2", "Irgm2", "Dnaja3", "Kynu", "Bst2", "Sec61a1",
"Cited1", "H2-Aa", "Slc11a1", "Gch1", "Ubd", "Il23r", "Slc30a8",
"Cxcl16", "Cyp27b1", "Trim21", "Ifitm2", "Ifitm1", "Ifitm3",
"Adamts13", "Tgtp2", "Tgtp1", "Snca", "Cd40", "Mefv", "Ciita",
"Klhl20", "Adar", "Ifnar2", "Eif2ak2", "Plscr1", "Lamp3", "Xaf1",
"Stat1", "S100a8", "Trdc", "S100a9", "Aim2", "Mb21d1", "Irf1",
"Pglyrp4", "Sdhaf4", "Ddx3x", "Defb1", "Serinc5", "Ddx58", "Defb12",
"Defb34", "Nlrp10", "Rela", "Gata3", "Csf1r", "Igha", "Defb15",
"Rnase6", "Defb35", "Defb13", "Pglyrp3", "Ighg2c", "Krt16", "Tril",
"Nlrp9c", "Bpifb3", "Pik3ca", "Nlrp1a", "Nlrp9b", "Ighg3", "Ighd",
"Ighm", "Nlrp1b", "Cfi", "Ly96", "Jchain", "C8b", "Nlrx1", "Cybb",
"Pml", "C8a", "Tnk1", "Polr3c", "Il1rap", "Rbm14", "Polr3e",
"Anxa1", "Cfd", "Ifnk", "Fcnb", "Nr1h4", "Defb43", "Nono", "5730559C18Rik",
"Hmgb3", "Defb30", "Sh2d1b2", "Ighv5-2", "Ighv2-2", "Tnfaip8l2",
"Tarm1", "Ighv5-4", "Bpifa1", "Nod2", "Ighv2-3", "Otulin", "Ighv5-6",
"Bpifb1", "Ifi202b", "Ighv5-9", "Fcgr1", "Trim28", "Arg1", "Trbc1",
"Pik3cg", "Defb18", "Defb41", "Ighv2-5", "Ighv5-12", "Ighv2-6",
"Ighv5-9-1", "Polr3g", "Cyld", "Sh2d1a", "Zap70", "Ifit3", "Rsad2",
"Msrb1", "Ifit1", "Cr1l", "Cfp", "Sp110", "Trbc2", "Casp4", "Nod1",
"Sla2", "Smpdl3b", "Tlr5", "Ighv5-12-4", "Ighv2-9-1", "Axl",
"Map3k5", "Cd55", "Blk", "Myd88", "Tnfsf4", "Ighv2-6-8", "Ighv2-7",
"Ighv5-15", "Ighv5-16", "Ighv5-17", "Ighv2-9", "Irf5", "Ighv7-1",
"Ighv7-2", "Ighv14-1", "Ighv4-1", "Ighv3-1", "Ighv11-1", "Trim11",
"Sla", "Ighv14-2", "Ighv11-2", "Ighv14-3", "Trim14", "Ighv16-1",
"Ighv9-1", "Dhx58", "Trim13", "Pqbp1", "Cyba", "Ighv9-2", "Ighv9-3",
"Ighv7-3", "Ankhd1", "Ighv14-4", "Ighv3-3", "Oasl2", "Ighv7-4",
"Ighv3-4", "Nrros", "Tlr13", "Fes", "Ticam1", "Ighv3-6", "Ighv9-4",
"Apobec3", "Ighv3-8", "Ighv13-2", "Ankrd17", "Trim15", "Ighv12-3",
"Ighv6-3", "Oasl1", "Trim10", "Tlr1", "Ighv6-4", "C4bp", "Trim5",
"Tlr6", "Trim31", "Polr3b", "Mavs", "Vnn1", "Apcs", "Akirin2",
"Ssc5d", "Trim12a", "Polr3d", "Clec5a", "Fgg", "Dab2ip", "Hmgb2",
"Fga", "Fgb", "Trim12c", "Fgr", "Ipo7", "Mst1r", "Trim30b", "Trim30c",
"Sfpq", "Il34", "Trim25", "Trim30a", "Trim30d", "Pspc1", "Card9",
"Mbl1", "Ptk2", "Lcn2", "Abl1", "Sftpd", "Tlr8", "Pycard", "Ighv6-5",
"Tlr2", "Ighv6-6", "Ighv6-7", "Serping1", "Tlr7", "Src", "Ighv8-2",
"Iglc1", "Ighv10-1", "Ighv1-4", "Il1f6", "Iglc4", "Ighv1-5",
"Ighv10-3", "Ighv1-7", "Iglc2", "Defb23", "Gper1", "Cnpy3", "Trdv4",
"Defb20", "Defb22", "Defb26", "Defb28", "Il1f5", "Defb29", "Ighv15-2",
"Ighv1-9", "Defb21", "Itch", "Pglyrp1", "Nfkb1", "Defb19", "Defb45",
"Il1rl2", "Defb36", "Mid2", "Defb25", "Ighv1-11", "Ighv1-12",
"Ighv1-15", "Ighv1-16", "Grb2", "Ighv1-18", "Ighv1-19", "Ighv1-20",
"Map4k2", "Il1f8", "Pik3cb", "Adam15", "Ighv1-22", "Bcl10", "Ighv1-23",
"Ighv1-24", "Ighv1-26", "Prkdc", "Irak4", "F2rl1", "Adgrb1",
"Isg20", "C1qb", "C1qc", "C1qa", "Mr1", "Polr3a", "Tlr9", "Ighv1-76",
"Optn", "Ighv1-77", "Ptx3", "Ighv1-78", "Zbtb1", "Grap", "Lbp",
"Arhgef2", "Mcoln2", "Ighv1-80", "Tbk1", "Ighv1-81", "Ifih1",
"Ighv1-31", "Bmx", "Ighv1-82", "Ighv1-34", "Ly86", "Nlrc5", "Ighv1-85",
"Polr3f", "Ighv1-36", "Ighv1-37", "Tlr11", "Trim27", "Malt1",
"Fadd", "Ighv1-39", "Ighv1-42", "Ighv1-43", "Btk", "Dcst1", "Parp14",
"Ighv1-47", "Ighv8-4", "Ighv1-49", "Itk", "Ighv8-5", "Ighv1-50",
"Trim26", "Chid1", "Csk", "Fcer1g", "Ighv1-52", "Ighv1-53", "Ighv8-6",
"Naip2", "Nlrp3", "C8g", "Ighv1-54", "Csf1", "Traf3", "Ighv1-55",
"Ighv1-56", "Elf4", "Dtx3l", "Irak1", "Naip6", "Parp9", "Ighv8-8",
"Ighv1-58", "Pik3cd", "Hck", "Ighv1-59", "Cd180", "Irf3", "Ighv1-61",
"Ighv1-62-1", "Ifnl2", "Relb", "Ifnl3", "Fyn", "Ighv1-62-2",
"Ighv1-62-3", "Hmgb1", "Tlr12", "Ighv8-9", "Yes1", "Ighv1-63",
"Ighv1-64", "Tbkbp1", "Siglecg", "Ighv8-11", "Ighv1-66", "Cfh",
"Zc3hav1", "Ighv1-67", "Ighv1-69", "Ighv8-12", "Havcr2", "4933415F23Rik",
"Trim32", "Ighv8-13", "C3", "Ighv1-74", "Trim62", "Hexim1", "Matr3",
"Ighv1-75", "Slpi", "Zfp809", "Igll1", "Arid5a", "Tollip", "Tlr4",
"Syk", "C4b", "Cd244", "Fbxo9", "Ly9", "Ifnb1", "Zfp683", "Ifna15",
"Ifna9", "Ifna14", "Ifna12", "Lck", "Serinc3", "Nlrp6", "Camp",
"Ifna13", "Ifna16", "Ifnab", "Lyn", "Gm13271", "Gm13283", "Gm13290",
"Gm13289", "Gm13272", "Cd24a", "Spon2", "Lgr4", "Ifnz", "Gm13276",
"Gm13277", "Gm13278", "Tlr3", "Gm13275", "Gm13279", "Gm13285",
"Gm13287", "Gm13288", "Ifna11", "Ifna4", "Oas2", "Prkd1", "Oas3",
"Slamf1", "Marco", "Ifne", "Rarres2", "Clec4a2", "Treml4", "Oas1a",
"Slamf6", "Fcna", "Sarm1", "Atg5", "Trim59", "Clec4n", "Herc6",
"Ripk2", "Irgm1", "Prdm1", "Hc", "Clec4d", "Clec4e", "Ticam2",
"Trem2", "Il27", "Frk", "Mif", "Ptk2b", "C1rl", "Irf7", "C1ra",
"C1rb", "C1s2", "Polr3k", "Zbp1", "Trim35", "Il23a", "C2", "Cfb",
"Padi4", "Cactin", "Masp2", "Klrg1", "Arg2", "Ptk6", "Clec7a",
"Rnf135", "Txk", "Klrk1", "Sec14l1", "Crcp", "Styk1", "Tmem173",
"Atg12", "Gm17416", "Iigp1", "Polr3h", "Riok3", "Cd14", "Gm5849",
"Mbl2", "Akap8", "Xrcc5", "Nfkb2", "Masp1", "Trim8", "Pcbp2",
"Ppp1r14b", "Gsdmd", "Fer", "Grap2", "Nlrc4", "Map4k2"), ensembl_gene_id = c("ENSMUSG00000032596",
"ENSMUSG00000027078", "ENSMUSG00000021774", "ENSMUSG00000058317",
"ENSMUSG00000035692", "ENSMUSG00000042349", "ENSMUSG00000044791",
"ENSMUSG00000038884", "ENSMUSG00000023341", "ENSMUSG00000025403",
"ENSMUSG00000069874", "ENSMUSG00000004069", "ENSMUSG00000026866",
"ENSMUSG00000046718", "ENSMUSG00000030082", "ENSMUSG00000051159",
"ENSMUSG00000036594", "ENSMUSG00000026177", "ENSMUSG00000037580",
"ENSMUSG00000035186", "ENSMUSG00000049093", "ENSMUSG00000022315",
"ENSMUSG00000018920", "ENSMUSG00000006724", "ENSMUSG00000030966",
"ENSMUSG00000060591", "ENSMUSG00000025491", "ENSMUSG00000025492",
"ENSMUSG00000014852", "ENSMUSG00000078921", "ENSMUSG00000078922",
"ENSMUSG00000025889", "ENSMUSG00000017652", "ENSMUSG00000022534",
"ENSMUSG00000022504", "ENSMUSG00000026705", "ENSMUSG00000027951",
"ENSMUSG00000022971", "ENSMUSG00000024079", "ENSMUSG00000032369",
"ENSMUSG00000041247", "ENSMUSG00000040483", "ENSMUSG00000026104",
"ENSMUSG00000056054", "ENSMUSG00000104876", "ENSMUSG00000056071",
"ENSMUSG00000037860", "ENSMUSG00000032344", "ENSMUSG00000018899",
"ENSMUSG00000042250", "ENSMUSG00000026154", "ENSMUSG00000000787",
"ENSMUSG00000044748", "ENSMUSG00000021703", "ENSMUSG00000040296",
"ENSMUSG00000043787", "ENSMUSG00000052554", "ENSMUSG00000049709",
"ENSMUSG00000024927", "ENSMUSG00000015619", "ENSMUSG00000024621",
"ENSMUSG00000095079", "ENSMUSG00000048500", "ENSMUSG00000021880",
"ENSMUSG00000058052", "ENSMUSG00000044222", "ENSMUSG00000042244",
"ENSMUSG00000076612", "ENSMUSG00000053797", "ENSMUSG00000043496",
"ENSMUSG00000040614", "ENSMUSG00000068008", "ENSMUSG00000027665",
"ENSMUSG00000069830", "ENSMUSG00000060508", "ENSMUSG00000076615",
"ENSMUSG00000104213", "ENSMUSG00000076617", "ENSMUSG00000070390",
"ENSMUSG00000058952", "ENSMUSG00000025779", "ENSMUSG00000067149",
"ENSMUSG00000029656", "ENSMUSG00000032109", "ENSMUSG00000015340",
"ENSMUSG00000036986", "ENSMUSG00000035031", "ENSMUSG00000001583",
"ENSMUSG00000028099", "ENSMUSG00000022514", "ENSMUSG00000006456",
"ENSMUSG00000030880", "ENSMUSG00000024659", "ENSMUSG00000061780",
"ENSMUSG00000042993", "ENSMUSG00000026835", "ENSMUSG00000047638",
"ENSMUSG00000075572", "ENSMUSG00000031311", "ENSMUSG00000041605",
"ENSMUSG00000015217", "ENSMUSG00000075571", "ENSMUSG00000073494",
"ENSMUSG00000076633", "ENSMUSG00000096464", "ENSMUSG00000013707",
"ENSMUSG00000053338", "ENSMUSG00000095612", "ENSMUSG00000027483",
"ENSMUSG00000055994", "ENSMUSG00000094164", "ENSMUSG00000046034",
"ENSMUSG00000094951", "ENSMUSG00000027485", "ENSMUSG00000026535",
"ENSMUSG00000095285", "ENSMUSG00000015947", "ENSMUSG00000005566",
"ENSMUSG00000019987", "ENSMUSG00000076490", "ENSMUSG00000020573",
"ENSMUSG00000073735", "ENSMUSG00000067773", "ENSMUSG00000096498",
"ENSMUSG00000095429", "ENSMUSG00000096670", "ENSMUSG00000095210",
"ENSMUSG00000035834", "ENSMUSG00000036712", "ENSMUSG00000005696",
"ENSMUSG00000026117", "ENSMUSG00000074896", "ENSMUSG00000020641",
"ENSMUSG00000075705", "ENSMUSG00000034459", "ENSMUSG00000016481",
"ENSMUSG00000001128", "ENSMUSG00000070034", "ENSMUSG00000076498",
"ENSMUSG00000033538", "ENSMUSG00000038058", "ENSMUSG00000027636",
"ENSMUSG00000028885", "ENSMUSG00000079164", "ENSMUSG00000103033",
"ENSMUSG00000095565", "ENSMUSG00000002602", "ENSMUSG00000071369",
"ENSMUSG00000026399", "ENSMUSG00000014453", "ENSMUSG00000032508",
"ENSMUSG00000026700", "ENSMUSG00000076646", "ENSMUSG00000096824",
"ENSMUSG00000094134", "ENSMUSG00000094194", "ENSMUSG00000095571",
"ENSMUSG00000096638", "ENSMUSG00000029771", "ENSMUSG00000076665",
"ENSMUSG00000076653", "ENSMUSG00000094509", "ENSMUSG00000076655",
"ENSMUSG00000093838", "ENSMUSG00000094533", "ENSMUSG00000020455",
"ENSMUSG00000022372", "ENSMUSG00000095583", "ENSMUSG00000096108",
"ENSMUSG00000095642", "ENSMUSG00000039853", "ENSMUSG00000076661",
"ENSMUSG00000096805", "ENSMUSG00000017830", "ENSMUSG00000035235",
"ENSMUSG00000031157", "ENSMUSG00000006519", "ENSMUSG00000094102",
"ENSMUSG00000096459", "ENSMUSG00000076652", "ENSMUSG00000024483",
"ENSMUSG00000076666", "ENSMUSG00000094029", "ENSMUSG00000029561",
"ENSMUSG00000076668", "ENSMUSG00000103939", "ENSMUSG00000052384",
"ENSMUSG00000033777", "ENSMUSG00000053158", "ENSMUSG00000047123",
"ENSMUSG00000076672", "ENSMUSG00000094322", "ENSMUSG00000009585",
"ENSMUSG00000076674", "ENSMUSG00000076671", "ENSMUSG00000055204",
"ENSMUSG00000050747", "ENSMUSG00000076676", "ENSMUSG00000076677",
"ENSMUSG00000041827", "ENSMUSG00000073400", "ENSMUSG00000044827",
"ENSMUSG00000094174", "ENSMUSG00000026405", "ENSMUSG00000060441",
"ENSMUSG00000051498", "ENSMUSG00000058063", "ENSMUSG00000034453",
"ENSMUSG00000037523", "ENSMUSG00000037440", "ENSMUSG00000026542",
"ENSMUSG00000028291", "ENSMUSG00000035279", "ENSMUSG00000066258",
"ENSMUSG00000000776", "ENSMUSG00000029915", "ENSMUSG00000033860",
"ENSMUSG00000026883", "ENSMUSG00000054717", "ENSMUSG00000028001",
"ENSMUSG00000033831", "ENSMUSG00000057143", "ENSMUSG00000028874",
"ENSMUSG00000066232", "ENSMUSG00000032584", "ENSMUSG00000052749",
"ENSMUSG00000078616", "ENSMUSG00000028820", "ENSMUSG00000031750",
"ENSMUSG00000000275", "ENSMUSG00000030921", "ENSMUSG00000057596",
"ENSMUSG00000021938", "ENSMUSG00000026928", "ENSMUSG00000037780",
"ENSMUSG00000022607", "ENSMUSG00000026822", "ENSMUSG00000026842",
"ENSMUSG00000021795", "ENSMUSG00000040522", "ENSMUSG00000030793",
"ENSMUSG00000096407", "ENSMUSG00000027995", "ENSMUSG00000076680",
"ENSMUSG00000087582", "ENSMUSG00000023224", "ENSMUSG00000044583",
"ENSMUSG00000027646", "ENSMUSG00000102301", "ENSMUSG00000105906",
"ENSMUSG00000095981", "ENSMUSG00000095442", "ENSMUSG00000026984",
"ENSMUSG00000106039", "ENSMUSG00000096499", "ENSMUSG00000095700",
"ENSMUSG00000095200", "ENSMUSG00000076937", "ENSMUSG00000074681",
"ENSMUSG00000053647", "ENSMUSG00000023973", "ENSMUSG00000076867",
"ENSMUSG00000049560", "ENSMUSG00000027468", "ENSMUSG00000074680",
"ENSMUSG00000074679", "ENSMUSG00000026983", "ENSMUSG00000044249",
"ENSMUSG00000076688", "ENSMUSG00000094694", "ENSMUSG00000056544",
"ENSMUSG00000027598", "ENSMUSG00000030413", "ENSMUSG00000028163",
"ENSMUSG00000050645", "ENSMUSG00000062124", "ENSMUSG00000070942",
"ENSMUSG00000044863", "ENSMUSG00000000266", "ENSMUSG00000074678",
"ENSMUSG00000102888", "ENSMUSG00000095416", "ENSMUSG00000103254",
"ENSMUSG00000095554", "ENSMUSG00000059923", "ENSMUSG00000076695",
"ENSMUSG00000096410", "ENSMUSG00000095761", "ENSMUSG00000024948",
"ENSMUSG00000026985", "ENSMUSG00000032462", "ENSMUSG00000028041",
"ENSMUSG00000094561", "ENSMUSG00000028191", "ENSMUSG00000103290",
"ENSMUSG00000094241", "ENSMUSG00000094546", "ENSMUSG00000022672",
"ENSMUSG00000059883", "ENSMUSG00000021678", "ENSMUSG00000034730",
"ENSMUSG00000039236", "ENSMUSG00000036905", "ENSMUSG00000036896",
"ENSMUSG00000036887", "ENSMUSG00000026471", "ENSMUSG00000025280",
"ENSMUSG00000045322", "ENSMUSG00000093896", "ENSMUSG00000026672",
"ENSMUSG00000096452", "ENSMUSG00000027832", "ENSMUSG00000096326",
"ENSMUSG00000033454", "ENSMUSG00000004837", "ENSMUSG00000016024",
"ENSMUSG00000028059", "ENSMUSG00000011008", "ENSMUSG00000094075",
"ENSMUSG00000020115", "ENSMUSG00000094689", "ENSMUSG00000026896",
"ENSMUSG00000096649", "ENSMUSG00000031377", "ENSMUSG00000095127",
"ENSMUSG00000093955", "ENSMUSG00000021423", "ENSMUSG00000074151",
"ENSMUSG00000096150", "ENSMUSG00000027427", "ENSMUSG00000094051",
"ENSMUSG00000095923", "ENSMUSG00000051969", "ENSMUSG00000021326",
"ENSMUSG00000032688", "ENSMUSG00000031077", "ENSMUSG00000095130",
"ENSMUSG00000094652", "ENSMUSG00000095859", "ENSMUSG00000031264",
"ENSMUSG00000042672", "ENSMUSG00000034422", "ENSMUSG00000076709",
"ENSMUSG00000096355", "ENSMUSG00000076710", "ENSMUSG00000020395",
"ENSMUSG00000102364", "ENSMUSG00000094198", "ENSMUSG00000024457",
"ENSMUSG00000025512", "ENSMUSG00000032312", "ENSMUSG00000058715",
"ENSMUSG00000095204", "ENSMUSG00000093894", "ENSMUSG00000094505",
"ENSMUSG00000078945", "ENSMUSG00000032691", "ENSMUSG00000015083",
"ENSMUSG00000094787", "ENSMUSG00000014599", "ENSMUSG00000021277",
"ENSMUSG00000095589", "ENSMUSG00000094862", "ENSMUSG00000031103",
"ENSMUSG00000049502", "ENSMUSG00000031392", "ENSMUSG00000078942",
"ENSMUSG00000022906", "ENSMUSG00000104452", "ENSMUSG00000095889",
"ENSMUSG00000039936", "ENSMUSG00000003283", "ENSMUSG00000095197",
"ENSMUSG00000021624", "ENSMUSG00000003184", "ENSMUSG00000094087",
"ENSMUSG00000102313", "ENSMUSG00000059128", "ENSMUSG00000002983",
"ENSMUSG00000060747", "ENSMUSG00000019843", "ENSMUSG00000096078",
"ENSMUSG00000096767", "ENSMUSG00000066551", "ENSMUSG00000062545",
"ENSMUSG00000095117", "ENSMUSG00000014932", "ENSMUSG00000096672",
"ENSMUSG00000094088", "ENSMUSG00000038517", "ENSMUSG00000030468",
"ENSMUSG00000095170", "ENSMUSG00000095519", "ENSMUSG00000026365",
"ENSMUSG00000029826", "ENSMUSG00000095863", "ENSMUSG00000094502",
"ENSMUSG00000076731", "ENSMUSG00000020399", "ENSMUSG00000073730",
"ENSMUSG00000051675", "ENSMUSG00000076733", "ENSMUSG00000024164",
"ENSMUSG00000094124", "ENSMUSG00000041000", "ENSMUSG00000048878",
"ENSMUSG00000037236", "ENSMUSG00000096020", "ENSMUSG00000017002",
"ENSMUSG00000057982", "ENSMUSG00000075370", "ENSMUSG00000037447",
"ENSMUSG00000025139", "ENSMUSG00000039005", "ENSMUSG00000021457",
"ENSMUSG00000073418", "ENSMUSG00000004709", "ENSMUSG00000001366",
"ENSMUSG00000004707", "ENSMUSG00000048806", "ENSMUSG00000049410",
"ENSMUSG00000096011", "ENSMUSG00000095270", "ENSMUSG00000095896",
"ENSMUSG00000073811", "ENSMUSG00000000409", "ENSMUSG00000017707",
"ENSMUSG00000038745", "ENSMUSG00000038357", "ENSMUSG00000063376",
"ENSMUSG00000078355", "ENSMUSG00000100079", "ENSMUSG00000042228",
"ENSMUSG00000094618", "ENSMUSG00000100505", "ENSMUSG00000094271",
"ENSMUSG00000096582", "ENSMUSG00000096591", "ENSMUSG00000047139",
"ENSMUSG00000037379", "ENSMUSG00000050199", "ENSMUSG00000096854",
"ENSMUSG00000099545", "ENSMUSG00000100234", "ENSMUSG00000101163",
"ENSMUSG00000031639", "ENSMUSG00000099518", "ENSMUSG00000099420",
"ENSMUSG00000095101", "ENSMUSG00000094648", "ENSMUSG00000070908",
"ENSMUSG00000100549", "ENSMUSG00000070904", "ENSMUSG00000032690",
"ENSMUSG00000002688", "ENSMUSG00000032661", "ENSMUSG00000015316",
"ENSMUSG00000026390", "ENSMUSG00000045364", "ENSMUSG00000009281",
"ENSMUSG00000030148", "ENSMUSG00000051682", "ENSMUSG00000052776",
"ENSMUSG00000015314", "ENSMUSG00000026938", "ENSMUSG00000050132",
"ENSMUSG00000038160", "ENSMUSG00000034317", "ENSMUSG00000023349",
"ENSMUSG00000029798", "ENSMUSG00000041135", "ENSMUSG00000046879",
"ENSMUSG00000038151", "ENSMUSG00000026874", "ENSMUSG00000030144",
"ENSMUSG00000030142", "ENSMUSG00000056130", "ENSMUSG00000023992",
"ENSMUSG00000044701", "ENSMUSG00000019779", "ENSMUSG00000033307",
"ENSMUSG00000059456", "ENSMUSG00000038527", "ENSMUSG00000025498",
"ENSMUSG00000055172", "ENSMUSG00000098470", "ENSMUSG00000079343",
"ENSMUSG00000038628", "ENSMUSG00000027514", "ENSMUSG00000022043",
"ENSMUSG00000025383", "ENSMUSG00000024371", "ENSMUSG00000090231",
"ENSMUSG00000025330", "ENSMUSG00000034889", "ENSMUSG00000028979",
"ENSMUSG00000030114", "ENSMUSG00000021125", "ENSMUSG00000038751",
"ENSMUSG00000079293", "ENSMUSG00000020707", "ENSMUSG00000054892",
"ENSMUSG00000030149", "ENSMUSG00000020823", "ENSMUSG00000025532",
"ENSMUSG00000032899", "ENSMUSG00000024349", "ENSMUSG00000032905",
"ENSMUSG00000090485", "ENSMUSG00000054072", "ENSMUSG00000022476",
"ENSMUSG00000024404", "ENSMUSG00000051439", "ENSMUSG00000096621",
"ENSMUSG00000024863", "ENSMUSG00000024045", "ENSMUSG00000026187",
"ENSMUSG00000025225", "ENSMUSG00000022887", "ENSMUSG00000025034",
"ENSMUSG00000056851", "ENSMUSG00000056612", "ENSMUSG00000022575",
"ENSMUSG00000000127", "ENSMUSG00000042351", "ENSMUSG00000039193",
"ENSMUSG00000106850")), .Names = c("mgi_symbol", "external_gene_name",
"ensembl_gene_id"), class = "data.frame", row.names = c(NA, -527L
))results_interferon_response_immuneRun PCA and t-SNE for the young vs old SmartSeq2 data
seurat_sms2 <- FindVariableGenes(object = seurat_sms2, mean.function = ExpMean, dispersion.function = LogVMR,
y.cutoff = 0.75)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
genes.var <- apply(X = seurat_sms2@raw.data , MARGIN = 1 , FUN = var)
genes.var.top <- names( sort(genes.var , decreasing = TRUE)[1:2000] )
seurat_sms2@var.genes <- genes.var.topseurat_sms2@imputed <- as.data.frame.matrix(seurat_sms2@data)seurat_sms2 <- RunPCA(object = seurat_sms2, pc.genes = seurat_sms2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5 , use.imputed = TRUE )[1] "PC1"
[1] "ENSMUSG00000028517" "ENSMUSG00000022037" "ENSMUSG00000002985" "ENSMUSG00000079037" "ENSMUSG00000006205"
[1] ""
[1] "ENSMUSG00000094627" "ENSMUSG00000047945" "ENSMUSG00000020737" "ENSMUSG00000003038" "ENSMUSG00000049775"
[1] ""
[1] ""
[1] "PC2"
[1] "ENSMUSG00000026385" "ENSMUSG00000001025" "ENSMUSG00000026701" "ENSMUSG00000037852" "ENSMUSG00000018451"
[1] ""
[1] "ENSMUSG00000038943" "ENSMUSG00000020897" "ENSMUSG00000019942" "ENSMUSG00000005233" "ENSMUSG00000020914"
[1] ""
[1] ""
[1] "PC3"
[1] "ENSMUSG00000035202" "ENSMUSG00000031548" "ENSMUSG00000026728" "ENSMUSG00000086859" "ENSMUSG00000021702"
[1] ""
[1] "ENSMUSG00000010095" "ENSMUSG00000033998" "ENSMUSG00000039542" "ENSMUSG00000028691" "ENSMUSG00000031467"
[1] ""
[1] ""
[1] "PC4"
[1] "ENSMUSG00000030867" "ENSMUSG00000020897" "ENSMUSG00000020808" "ENSMUSG00000027496" "ENSMUSG00000030677"
[1] ""
[1] "ENSMUSG00000101249" "ENSMUSG00000100862" "ENSMUSG00000083563" "ENSMUSG00000101111" "ENSMUSG00000082179"
[1] ""
[1] ""
[1] "PC5"
[1] "ENSMUSG00000017677" "ENSMUSG00000030342" "ENSMUSG00000032046" "ENSMUSG00000097971" "ENSMUSG00000025381"
[1] ""
[1] "ENSMUSG00000057666" "ENSMUSG00000031762" "ENSMUSG00000028691" "ENSMUSG00000083076" "ENSMUSG00000035885"
[1] ""
[1] ""seurat_sms2 <- ProjectPCA(object = seurat_sms2 )[1] "PC1"
[1] "ENSMUSG00000028517" "ENSMUSG00000022037" "ENSMUSG00000002985" "ENSMUSG00000079037" "ENSMUSG00000006205"
[6] "ENSMUSG00000041329" "ENSMUSG00000050953" "ENSMUSG00000027447" "ENSMUSG00000026424" "ENSMUSG00000037706"
[11] "ENSMUSG00000031517" "ENSMUSG00000005360" "ENSMUSG00000017390" "ENSMUSG00000058254" "ENSMUSG00000045092"
[16] "ENSMUSG00000022132" "ENSMUSG00000029309" "ENSMUSG00000004892" "ENSMUSG00000020591" "ENSMUSG00000030428"
[21] "ENSMUSG00000028128" "ENSMUSG00000005089" "ENSMUSG00000027574" "ENSMUSG00000026249" "ENSMUSG00000031760"
[26] "ENSMUSG00000032883" "ENSMUSG00000007097" "ENSMUSG00000030310" "ENSMUSG00000022564" "ENSMUSG00000039533"
[1] ""
[1] "ENSMUSG00000094627" "ENSMUSG00000047945" "ENSMUSG00000070713" "ENSMUSG00000020737" "ENSMUSG00000049775"
[6] "ENSMUSG00000000184" "ENSMUSG00000003038" "ENSMUSG00000046434" "ENSMUSG00000001525" "ENSMUSG00000026238"
[11] "ENSMUSG00000096544" "ENSMUSG00000028832" "ENSMUSG00000079523" "ENSMUSG00000067274" "ENSMUSG00000094790"
[16] "ENSMUSG00000004530" "ENSMUSG00000015217" "ENSMUSG00000032518" "ENSMUSG00000054717" "ENSMUSG00000026355"
[21] "ENSMUSG00000028639" "ENSMUSG00000042462" "ENSMUSG00000002870" "ENSMUSG00000076437" "ENSMUSG00000049124"
[26] "ENSMUSG00000098318" "ENSMUSG00000006728" "ENSMUSG00000020649" "ENSMUSG00000044533" "ENSMUSG00000040274"
[1] ""
[1] ""
[1] "PC2"
[1] "ENSMUSG00000026385" "ENSMUSG00000001025" "ENSMUSG00000027523" "ENSMUSG00000001270" "ENSMUSG00000018567"
[6] "ENSMUSG00000026701" "ENSMUSG00000029446" "ENSMUSG00000018451" "ENSMUSG00000026728" "ENSMUSG00000029455"
[11] "ENSMUSG00000037852" "ENSMUSG00000076441" "ENSMUSG00000021702" "ENSMUSG00000053398" "ENSMUSG00000053931"
[16] "ENSMUSG00000024661" "ENSMUSG00000040997" "ENSMUSG00000030122" "ENSMUSG00000033059" "ENSMUSG00000024425"
[21] "ENSMUSG00000031839" "ENSMUSG00000068523" "ENSMUSG00000074457" "ENSMUSG00000067818" "ENSMUSG00000030934"
[26] "ENSMUSG00000044080" "ENSMUSG00000026185" "ENSMUSG00000031812" "ENSMUSG00000036854" "ENSMUSG00000031950"
[1] ""
[1] "ENSMUSG00000070495" "ENSMUSG00000034120" "ENSMUSG00000085453" "ENSMUSG00000085334" "ENSMUSG00000002297"
[6] "ENSMUSG00000102443" "ENSMUSG00000042501" "ENSMUSG00000096111" "ENSMUSG00000021998" "ENSMUSG00000056888"
[11] "ENSMUSG00000021097" "ENSMUSG00000102718" "ENSMUSG00000093684" "ENSMUSG00000092814" "ENSMUSG00000020717"
[16] "ENSMUSG00000069892" "ENSMUSG00000024411" "ENSMUSG00000075307" "ENSMUSG00000097452" "ENSMUSG00000085635"
[21] "ENSMUSG00000096061" "ENSMUSG00000040459" "ENSMUSG00000097156" "ENSMUSG00000097384" "ENSMUSG00000050097"
[26] "ENSMUSG00000010095" "ENSMUSG00000100131" "ENSMUSG00000036815" "ENSMUSG00000104174" "ENSMUSG00000025758"
[1] ""
[1] ""
[1] "PC3"
[1] "ENSMUSG00000104211" "ENSMUSG00000102470" "ENSMUSG00000102858" "ENSMUSG00000035202" "ENSMUSG00000050097"
[6] "ENSMUSG00000104069" "ENSMUSG00000026185" "ENSMUSG00000070495" "ENSMUSG00000086607" "ENSMUSG00000049723"
[11] "ENSMUSG00000096061" "ENSMUSG00000056888" "ENSMUSG00000042501" "ENSMUSG00000092814" "ENSMUSG00000093684"
[16] "ENSMUSG00000104329" "ENSMUSG00000017652" "ENSMUSG00000056025" "ENSMUSG00000096111" "ENSMUSG00000086189"
[21] "ENSMUSG00000081305" "ENSMUSG00000102599" "ENSMUSG00000014725" "ENSMUSG00000081974" "ENSMUSG00000036109"
[26] "ENSMUSG00000039982" "ENSMUSG00000101462" "ENSMUSG00000026728" "ENSMUSG00000027670" "ENSMUSG00000005493"
[1] ""
[1] "ENSMUSG00000033998" "ENSMUSG00000010095" "ENSMUSG00000031467" "ENSMUSG00000036949" "ENSMUSG00000039542"
[6] "ENSMUSG00000028691" "ENSMUSG00000057666" "ENSMUSG00000047557" "ENSMUSG00000022477" "ENSMUSG00000000088"
[11] "ENSMUSG00000030317" "ENSMUSG00000061904" "ENSMUSG00000021508" "ENSMUSG00000023456" "ENSMUSG00000003974"
[16] "ENSMUSG00000079592" "ENSMUSG00000048076" "ENSMUSG00000060424" "ENSMUSG00000058927" "ENSMUSG00000049612"
[21] "ENSMUSG00000028655" "ENSMUSG00000030235" "ENSMUSG00000033208" "ENSMUSG00000047786" "ENSMUSG00000032281"
[26] "ENSMUSG00000021613" "ENSMUSG00000032014" "ENSMUSG00000024091" "ENSMUSG00000023175" "ENSMUSG00000028757"
[1] ""
[1] ""
[1] "PC4"
[1] "ENSMUSG00000041556" "ENSMUSG00000021702" "ENSMUSG00000036570" "ENSMUSG00000041577" "ENSMUSG00000053398"
[6] "ENSMUSG00000027875" "ENSMUSG00000031137" "ENSMUSG00000018451" "ENSMUSG00000037003" "ENSMUSG00000031428"
[11] "ENSMUSG00000052188" "ENSMUSG00000059970" "ENSMUSG00000044550" "ENSMUSG00000027215" "ENSMUSG00000000346"
[16] "ENSMUSG00000037852" "ENSMUSG00000064220" "ENSMUSG00000024640" "ENSMUSG00000053931" "ENSMUSG00000030934"
[21] "ENSMUSG00000008393" "ENSMUSG00000066058" "ENSMUSG00000022489" "ENSMUSG00000021268" "ENSMUSG00000054400"
[26] "ENSMUSG00000022037" "ENSMUSG00000009281" "ENSMUSG00000046731" "ENSMUSG00000033059" "ENSMUSG00000015837"
[1] ""
[1] "ENSMUSG00000101249" "ENSMUSG00000100862" "ENSMUSG00000083563" "ENSMUSG00000101111" "ENSMUSG00000082179"
[6] "ENSMUSG00000083219" "ENSMUSG00000096449" "ENSMUSG00000102070" "ENSMUSG00000067736" "ENSMUSG00000081043"
[11] "ENSMUSG00000083076" "ENSMUSG00000100131" "ENSMUSG00000101939" "ENSMUSG00000096887" "ENSMUSG00000020122"
[16] "ENSMUSG00000083863" "ENSMUSG00000097346" "ENSMUSG00000074800" "ENSMUSG00000056612" "ENSMUSG00000067038"
[21] "ENSMUSG00000031320" "ENSMUSG00000086841" "ENSMUSG00000024387" "ENSMUSG00000049751" "ENSMUSG00000040722"
[26] "ENSMUSG00000035049" "ENSMUSG00000086583" "ENSMUSG00000081051" "ENSMUSG00000092902" "ENSMUSG00000100865"
[1] ""
[1] ""
[1] "PC5"
[1] "ENSMUSG00000017677" "ENSMUSG00000030342" "ENSMUSG00000032046" "ENSMUSG00000027303" "ENSMUSG00000001467"
[6] "ENSMUSG00000097971" "ENSMUSG00000025381" "ENSMUSG00000021670" "ENSMUSG00000022351" "ENSMUSG00000049339"
[11] "ENSMUSG00000021252" "ENSMUSG00000022122" "ENSMUSG00000019818" "ENSMUSG00000025203" "ENSMUSG00000027620"
[16] "ENSMUSG00000022199" "ENSMUSG00000020570" "ENSMUSG00000099615" "ENSMUSG00000030062" "ENSMUSG00000022812"
[21] "ENSMUSG00000031604" "ENSMUSG00000037608" "ENSMUSG00000030876" "ENSMUSG00000034126" "ENSMUSG00000024150"
[26] "ENSMUSG00000102918" "ENSMUSG00000020580" "ENSMUSG00000029778" "ENSMUSG00000093930" "ENSMUSG00000034210"
[1] ""
[1] "ENSMUSG00000074280" "ENSMUSG00000057666" "ENSMUSG00000018509" "ENSMUSG00000082691" "ENSMUSG00000073002"
[6] "ENSMUSG00000083911" "ENSMUSG00000031762" "ENSMUSG00000081254" "ENSMUSG00000028572" "ENSMUSG00000041115"
[11] "ENSMUSG00000028691" "ENSMUSG00000036246" "ENSMUSG00000075390" "ENSMUSG00000035885" "ENSMUSG00000082072"
[16] "ENSMUSG00000083076" "ENSMUSG00000097148" "ENSMUSG00000081964" "ENSMUSG00000039201" "ENSMUSG00000004610"
[21] "ENSMUSG00000066478" "ENSMUSG00000050856" "ENSMUSG00000095600" "ENSMUSG00000025486" "ENSMUSG00000060288"
[26] "ENSMUSG00000004085" "ENSMUSG00000022820" "ENSMUSG00000083564" "ENSMUSG00000022323" "ENSMUSG00000027835"
[1] ""
[1] ""seurat_sms2 <- JackStraw(object = seurat_sms2 )PCElbowPlot(object = seurat_sms2)
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type")PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_sms2 , dim.1 = 2, dim.2 = 3)
seurat_sms2 <- RunTSNE(seurat_sms2, dims.use = c(1,2,3) , do.fast = T , seed.use = 1)seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type")TSNEPlot(object = seurat_sms2)
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "age")TSNEPlot(object = seurat_sms2)
PCA plot PC1 vs PC2 with color
seurat_sms2@meta.data$type_long <- seurat_sms2@meta.data$type
seurat_sms2@meta.data$type_long <- recode_factor( seurat_sms2@meta.data$type_long , q1 = "qNSC1", q2 = "qNSC2", a1 = "aNSC1" , a2 = "aNSC2" )seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type_long")PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2 , cols.use = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , pt.shape = "age")
gg_pca <- PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2 , cols.use = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , pt.shape = "age", do.return = TRUE)gg_pca_2 <- ggplot(data = gg_pca$data , aes(x = x , y = y , color = ident , shape = pt.shape)) + geom_point() + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_color_manual(values = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , limits = c("qNSC1","qNSC2","aNSC1","aNSC2") , "Activation state") + scale_shape_manual(values = c(old = 17 , young = 16) , "Age") + coord_equal() + xlab("PC1") + ylab("PC2")
print(gg_pca_2)
sd <- GetDimReduction(object = seurat_sms2 , reduction.type = "pca" , slot = "sdev")
pc_percentage_of_variance <- round( (sd^2/sum(sd^2))*100 , digits = 1 )
gg_pca_hollowcircle <- ggplot(data = gg_pca$data , aes(x = x , y = y , color = ident , shape = pt.shape)) + geom_point() + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_color_manual(values = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , limits = c("qNSC1","qNSC2","aNSC1","aNSC2") , "Activation state") + scale_shape_manual(values = c(old = 17 , young = 1) , "Age") + coord_equal() + xlab( paste("PC1 (",pc_percentage_of_variance[1],"%)") ) + ylab( paste("PC2 (",pc_percentage_of_variance[2],"%)" )) + coord_equal()
print(gg_pca_hollowcircle)
gg_pca_3 <- ggplot(data = gg_pca$data , aes(x = x , y = y , fill = factor(ident, levels = c("qNSC1","qNSC2","aNSC1","aNSC2")) , shape = pt.shape) ) + geom_point(size = 2, color = "black" ) + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_fill_manual(values = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , limits = c("qNSC1","qNSC2","aNSC1","aNSC2") , "Activation state") + scale_shape_manual(values = c(old = 24 , young = 21) , "Age") + coord_equal() + xlab( paste("PC1 (",pc_percentage_of_variance[1],"%)") ) + ylab( paste("PC2 (",pc_percentage_of_variance[2],"%)" )) + guides(fill = guide_legend(override.aes=list(shape=21)))
print(gg_pca_3)
gg_pca_age <- ggplot(data = gg_pca$data , aes(x = x , y = y , fill = pt.shape , shape = pt.shape) ) + geom_point(size = 2, color = "black" ) + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_fill_manual(values = c( "old" = "slateblue", "young" = "yellowgreen") , labels = c( "old" , "young" ) , name = "Age" ) + scale_shape_manual(values = c(old = 24 , young = 21) , "Age") + coord_equal() + xlab( paste("PC1 (",pc_percentage_of_variance[1],"%)") ) + ylab( paste("PC2 (",pc_percentage_of_variance[2],"%)" )) + guides(fill = guide_legend(override.aes=list(shape=21)))
print(gg_pca_age)
Make PCA plots for each subpopulation
age_colors <- c(young = "yellowgreen" , old = "slateblue")PCAPlot_seurat <- function(object , dim1 = 1, dim2 = 2 , scale_pcs_by_sd = TRUE , pt.size = 2){
gg_pca <- PCAPlot(object = object , dim.1 = dim1, dim.2 = dim2 , cols.use = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , pt.shape = "age", do.return = TRUE )
sd <- GetDimReduction(object = object , reduction.type = "pca" , slot = "sdev")
pov <- round( (sd^2/sum(sd^2))*100 , digits = 1 )
if(scale_pcs_by_sd){
gg_pca$data$x <- gg_pca$data$x * sd[[dim1]]
gg_pca$data$y <- gg_pca$data$y * sd[[dim2]]
}
xlimits <- NULL
ylimits <- NULL
x_range <- range(gg_pca$data$x)
x_diff <- x_range[2]-x_range[1]
y_range <- range(gg_pca$data$y)
y_diff <- y_range[2]-y_range[1]
if(x_diff > y_diff){
offset <- (( x_diff - y_diff ) / 2 )
y_range[1] <- y_range[1] - offset
y_range[2] <- y_range[2] + offset
ylimits <- y_range
}else if( y_diff > x_diff ){
offset <- (( y_diff - x_diff ) / 2 )
x_range[1] <- x_range[1] - offset
x_range[2] <- x_range[2] + offset
xlimits <- x_range
}
gg <- ggplot(data = gg_pca$data , aes(x = x , y = y , fill = pt.shape , shape = pt.shape) ) + geom_point(size = pt.size, colour = "black") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_fill_manual(values = age_colors , limits = names(age_colors) , name = "Age") + scale_shape_manual(values = c(old = 24 , young = 21) , limits = names(age_colors) , name = "Age") + coord_equal() + xlab(paste("PC",dim1," (",pov[dim1],"%)")) + ylab(paste("PC",dim2," (",pov[dim2], "%)")) + ggtitle(paste0(unique(gg_pca$data$ident)) ) + guides(color = "none")
if(!is.null(xlimits)){gg <- gg + xlim( xlimits )}
if(!is.null(ylimits)){gg <- gg + ylim( ylimits )}
gg
}TSNEPlot_Seurat <- function(x , title = ""){
xlimits <- NULL
ylimits <- NULL
x_range <- range(x$tSNE_1)
x_diff <- x_range[2]-x_range[1]
y_range <- range(x$tSNE_2)
y_diff <- y_range[2]-y_range[1]
if(x_diff > y_diff){
offset <- (( x_diff - y_diff ) / 2 )
y_range[1] <- y_range[1] - offset
y_range[2] <- y_range[2] + offset
ylimits <- y_range
}else if( y_diff > x_diff ){
offset <- (( y_diff - x_diff ) / 2 )
x_range[1] <- x_range[1] - offset
x_range[2] <- x_range[2] + offset
xlimits <- x_range
}
gg <- ggplot(data = x , mapping = aes(x = tSNE_1 , y = tSNE_2 , fill = age , shape = age)) + geom_point(size = 2, colour = "black") + scale_fill_manual(values = c( "old" = "slateblue", "young" = "yellowgreen") , labels = c( "old" , "young" ) , name = "Age" ) + labs( x = "tSNE 1" , y = "tSNE 2" ) + coord_equal() + guides(color = "none") + scale_shape_manual(values = c( "old" = 24 , "young" = 21) , labels = c( "old" , "young" ) , name = "Age") + ggtitle( title )
if(!is.null(xlimits)){gg <- gg + xlim( xlimits )}
if(!is.null(ylimits)){gg <- gg + ylim( ylimits )}
gg
}qNSC1
seurat_sms2_qNSC1 <- SubsetData(object = seurat_sms2 , ident.use = "qNSC1")
seurat_sms2_qNSC1 <- FindVariableGenes(object = seurat_sms2_qNSC1 , mean.function = ExpMean, dispersion.function = LogVMR)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
seurat_sms2_qNSC1 <- RunPCA(object = seurat_sms2_qNSC1 , do.print = FALSE )
pca_q1 <- PCAPlot_seurat( object = seurat_sms2_qNSC1 , dim1 = 1, dim2 = 2 )
pca_q1
PCAPlot_seurat( object = seurat_sms2_qNSC1 , dim1 = 2, dim2 = 3 )
PC_top50genes_qNSC1<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) , FUN=function(x){PCTopGenes(object = seurat_sms2_qNSC1 , pc.use = x , num.genes = 50 )} ) )
PCElbowPlot(object = seurat_sms2_qNSC1)
seurat_sms2_qNSC1 <- RunTSNE(object = seurat_sms2_qNSC1 , dims.use = 1:4 , seed.use = 1 )
TSNEPlot(object = seurat_sms2_qNSC1 ) 
x <- FetchData( object = seurat_sms2_qNSC1 , vars.all = c("tSNE_1","tSNE_2","age") )
gg_tsne_qNSC1 <- TSNEPlot_Seurat(x = x , title = "qNSC1")
gg_tsne_qNSC1
qNSC2
seurat_sms2_qNSC2 <- SubsetData(object = seurat_sms2 , ident.use = "qNSC2")
seurat_sms2_qNSC2 <- FindVariableGenes(object = seurat_sms2_qNSC2 , mean.function = ExpMean, dispersion.function = LogVMR)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
seurat_sms2_qNSC2 <- RunPCA(object = seurat_sms2_qNSC2 , do.print = FALSE)You're computing a large percentage of total singular values, standard svd might work better!pca_q2 <- PCAPlot_seurat( object = seurat_sms2_qNSC2 , dim1 = 1, dim2 = 2 )
pca_q2
PCAPlot_seurat( object = seurat_sms2_qNSC2 , dim1 = 2, dim2 = 3 )
PC_top50genes_qNSC2<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) , FUN=function(x){PCTopGenes(object = seurat_sms2_qNSC2 , pc.use = x , num.genes = 50 )} ) )
PCElbowPlot(object = seurat_sms2_qNSC2)
seurat_sms2_qNSC2 <- RunTSNE(object = seurat_sms2_qNSC2 , dims.use = 1:5 , seed.use = 1 , perplexity = 13 )
TSNEPlot(object = seurat_sms2_qNSC2 ) 
x <- FetchData( object = seurat_sms2_qNSC2 , vars.all = c("tSNE_1","tSNE_2","age") )
gg_tsne_qNSC2 <- TSNEPlot_Seurat(x = x , title = "qNSC2 (perplexity = 13)")
gg_tsne_qNSC2
aNSC1
seurat_sms2_aNSC1 <- SubsetData(object = seurat_sms2 , ident.use = "aNSC1")
seurat_sms2_aNSC1 <- FindVariableGenes(object = seurat_sms2_aNSC1 , mean.function = ExpMean, dispersion.function = LogVMR)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
seurat_sms2_aNSC1 <- RunPCA(object = seurat_sms2_aNSC1 , do.print = FALSE)You're computing a large percentage of total singular values, standard svd might work better!pca_a1 <- PCAPlot_seurat( object = seurat_sms2_aNSC1 , dim1 = 1, dim2 = 2 )
pca_a1
PCAPlot_seurat( object = seurat_sms2_aNSC1 , dim1 = 2, dim2 = 3 )
PC_top50genes_aNSC1<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) , FUN=function(x){PCTopGenes(object = seurat_sms2_aNSC1 , pc.use = x , num.genes = 50 )} ) )
PCElbowPlot(object = seurat_sms2_aNSC1)
seurat_sms2_aNSC1 <- RunTSNE(object = seurat_sms2_aNSC1 , dims.use = 1:5 , seed.use = 1 , perplexity = 7 )
TSNEPlot(object = seurat_sms2_aNSC1 ) 
x <- FetchData( object = seurat_sms2_aNSC1 , vars.all = c("tSNE_1","tSNE_2","age") )
gg_tsne_aNSC1 <- TSNEPlot_Seurat(x = x , title = "aNSC1 (perplexity = 7)")
gg_tsne_aNSC1
aNSC2
seurat_sms2_aNSC2 <- SubsetData(object = seurat_sms2 , ident.use = "aNSC2")
seurat_sms2_aNSC2 <- FindVariableGenes(object = seurat_sms2_aNSC2 , mean.function = ExpMean, dispersion.function = LogVMR)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
seurat_sms2_aNSC2 <- RunPCA(object = seurat_sms2_aNSC2 , do.print = FALSE)You're computing a large percentage of total singular values, standard svd might work better!pca_a2 <- PCAPlot_seurat( object = seurat_sms2_aNSC2 , dim1 = 1, dim2 = 2 )
pca_a2
PCAPlot_seurat( object = seurat_sms2_aNSC2 , dim1 = 2, dim2 = 3 )
PC_top50genes_aNSC2<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) , FUN=function(x){PCTopGenes(object = seurat_sms2_aNSC2 , pc.use = x , num.genes = 50 )} ) )
PCElbowPlot(object = seurat_sms2_aNSC2)
seurat_sms2_aNSC2 <- RunTSNE(object = seurat_sms2_aNSC2 , dims.use = 1:5 , seed.use = 1 , perplexity = 7 )
TSNEPlot(object = seurat_sms2_aNSC2 ) 
x <- FetchData( object = seurat_sms2_aNSC2 , vars.all = c("tSNE_1","tSNE_2","age") )
gg_tsne_aNSC2 <- TSNEPlot_Seurat(x = x , title = "aNSC2 (perplexity = 7)")
gg_tsne_aNSC2
grid.arrange(pca_q1,pca_q2,pca_a1,pca_a2)
t-SNE plots of subpopulations
Gather all t-SNE plots
grid.arrange( gg_tsne_qNSC1 , gg_tsne_qNSC2 , gg_tsne_aNSC1 , gg_tsne_aNSC2 )
saveRDS(object = seurat_sms2 , file = "seurat_sms2.RDS" )Find celltype markers
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type_long")
celltype_markers <- FindAllMarkers(object = seurat_sms2 ,test.use = "t" , random.seed = 123 )
|+ | 1 % ~32s
|++ | 2 % ~27s
|++ | 3 % ~26s
|+++ | 4 % ~24s
|+++ | 5 % ~23s
|++++ | 6 % ~23s
|++++ | 7 % ~22s
|+++++ | 8 % ~22s
|+++++ | 9 % ~21s
|++++++ | 10% ~21s
|++++++ | 11% ~21s
|+++++++ | 12% ~20s
|+++++++ | 13% ~20s
|++++++++ | 14% ~20s
|++++++++ | 15% ~19s
|+++++++++ | 16% ~19s
|+++++++++ | 17% ~19s
|++++++++++ | 18% ~19s
|++++++++++ | 19% ~18s
|+++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|++++++++++++ | 22% ~18s
|++++++++++++ | 23% ~17s
|+++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~17s
|++++++++++++++ | 26% ~17s
|++++++++++++++ | 27% ~16s
|+++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~16s
|++++++++++++++++ | 30% ~16s
|++++++++++++++++ | 31% ~15s
|+++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|++++++++++++++++++ | 34% ~15s
|++++++++++++++++++ | 35% ~14s
|+++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|++++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~14s
|+++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|++++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~13s
|+++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|++++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|+++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 09scelltype_markersSave markers of individual celltypes
celltypes <- c("qNSC1","qNSC2","aNSC1","aNSC2")
for(i in celltypes){
write.csv(x = celltype_markers %>% filter(cluster == i) , file = file.path("celltype_markers/",paste0("celltype_markers_",i,"_SmartSeq2.csv")) )
}GO analysis clusterCompare celltypes
DE_results_list <- list()
for(i in celltypes){
DE_results_list[[i]] <- celltype_markers %>% filter(cluster == i)
}GO term analysis function
GO_analysis <- function(DE_results_list , p_adj_cutoff = 0.05 ){
require("clusterProfiler")
if( length(DE_results_list) < 1){
warning("No entries in DE_results_list")
invisible(DE_results_list)
}
de_genes_list <-list(NULL)
for(i in seq_len(length(DE_results_list)) ){
de_genes <- DE_results_list[[i]] %>% filter( avg_logFC > 0 ) %>% dplyr::filter( p_val_adj < p_adj_cutoff ) %>% dplyr::pull(gene)
de_genes_list[[ names(DE_results_list[i]) ]] <- bitr(geneID = de_genes , fromType = "ENSEMBL" , toType = "ENTREZID" , OrgDb = "org.Mm.eg.db" )[,"ENTREZID"]
}
# available ontologies are:
# BP - biological_process
# CC - cellular_component
# MF - molecular_function
go_results <- compareCluster(geneClusters = de_genes_list , fun = "enrichGO" , OrgDb = "org.Mm.eg.db" , ont = "BP", pvalueCutoff = 0.05 )
return(go_results)
}smartseq2_compare_GO_results <- GO_analysis(DE_results_list = DE_results_list , p_adj_cutoff = 0.01)'select()' returned 1:many mapping between keys and columns
1.18% of input gene IDs are fail to map...'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
7% of input gene IDs are fail to map...'select()' returned 1:many mapping between keys and columns
3.31% of input gene IDs are fail to map...dotplot( smartseq2_compare_GO_results , showCategory = 10)
smartseq2_compare_GO_results.sim <- clusterProfiler::simplify(smartseq2_compare_GO_results) gg2 <- clusterProfiler::dotplot(smartseq2_compare_GO_results , showCategory = 15)
gg2$data$Description <- fct_relabel( .f = gg2$data$Description , .fun = function(x){str_wrap(string = x , width = 40)} )
plotdata <- gg2$data %>% filter( p.adjust < 0.05)
levels(plotdata$Cluster) <- sub( x = levels(plotdata$Cluster) , pattern = "DE_celltype_genes_", replacement = "")
gg_clusterCompare <- ggplot(data = plotdata , mapping = aes( x = Cluster , y = Description , size = GeneRatio , color = -log10(p.adjust) ) ) +
geom_point() +
scale_color_gradient(low = "blue" , high = "red" , breaks = c(1,10,20,30,40,50,60) , limits = c(1,60) ) +
theme_bw() +
theme( axis.text.x = element_text(colour="black",size=11), axis.text.y = element_text(colour="black",size=11, hjust = 1 ) )
gg_clusterCompare
gg_clusterCompare_center <- ggplot(data = plotdata , mapping = aes( x = Cluster , y = Description , size = GeneRatio , color = -log10(p.adjust) ) ) +
geom_point() +
scale_color_gradient(low = "blue" , high = "red" , breaks = c(1,10,20,30,40,50,60) , limits = c(1,60) ) +
theme_bw() +
theme( axis.text.x = element_text(colour="black",size=11), axis.text.y = element_text(colour="black",size=8, hjust = 0.5 ) )
gg_clusterCompare_center
Heatmap of DNA damage response genes
Load the list of DNA damage response genes.
dna_damage_genes <- read.csv("gene_lists/DNA_damage_genes.csv", stringsAsFactors = FALSE )
#readxl::read_xlsx(path = "gene_lists/dna damage genes for Sheng.xlsx" , col_names = TRUE ) %>% dplyr::filter( ! is.na(ensembl_gene_id) & gene_symbol != "TREX2")Convert the Module column to factor.
dna_damage_genes$module <- as.factor(dna_damage_genes$module)dna_damage_genesGet the TPM gene expression values for these genes from the TPM table
TPM_dna_damage_genes <- TPM_NSCs %>% dplyr::filter( ensembl_gene_id %in% dna_damage_genes$ensembl_gene_id )Combine the two data.frames
TPM_combined <- left_join(x = dna_damage_genes , y = TPM_dna_damage_genes , by = "ensembl_gene_id" )Column `ensembl_gene_id` joining character vector and factor, coercing into character vectorhead(TPM_combined)cell_order <- pseudotime_ordering %>% mutate( typesort = as.character(fct_recode(.f = type , c = "a1" , d = "a2" , a = "q1" , b = "q2") ) ) %>% group_by(age) %>% arrange(age, typesort) %>% pull(cell) %>% as.character()library(pheatmap)
TPM_mat <- as.matrix(TPM_combined %>% dplyr::select( c( "ensembl_gene_id" , cell_order ) ) %>% column_to_rownames("ensembl_gene_id"))
TPM_mat_names <- as.matrix(TPM_combined %>% dplyr::select( c( "gene_symbol" , cell_order ) ) %>% column_to_rownames("gene_symbol"))
anno_row <- dplyr::select(dna_damage_genes , module , ensembl_gene_id ) %>% column_to_rownames("ensembl_gene_id")
anno_col <- NSCs_annotation %>% dplyr::select(-cell)Plot log transformed TPM values
pheatmap(
mat = pmin( log( TPM_mat + 1 ) , 7 ) ,
cluster_rows = FALSE ,
cluster_cols = FALSE ,
scale = "none" ,
breaks = seq(0,7,length.out = 101) ,
color = viridisLite::viridis(n = 100) ,
border_color = NA ,
# gaps_row = row_breaks[seq_len(length.out = length(row_breaks) - 1 )] ,
annotation_col = anno_col ,
annotation_colors = list(
type = c(q1 = "steelblue" , q2 = "steelblue1" , a1 = "tomato" , a2 = "sienna1"),
age = c( young = "yellowgreen" , old = "slateblue")
)
)
Determine gaps between categories
row_breaks <- cumsum( anno_row %>% group_by(module) %>% mutate( num = length(module)) %>% unique() %>% pull(num) ) Plot log transformed TPM values with gene names as rownames
anno_col_rename <- anno_col
anno_col_rename$type <- as.character(anno_col_rename$type)
anno_col_rename$type[anno_col_rename$type == "q1"] <- "qNSC1"
anno_col_rename$type[anno_col_rename$type == "q2"] <- "qNSC2"
anno_col_rename$type[anno_col_rename$type == "a1"] <- "aNSC1"
anno_col_rename$type[anno_col_rename$type == "a2"] <- "aNSC2"
anno_col_rename$type <- factor(x = anno_col_rename$type , levels = c("qNSC1","qNSC2","aNSC1","aNSC2") ) # scale_white_red <- colorRampPalette(colors = RColorBrewer::brewer.pal(n = 9 , name = "Reds") )
scale_white_red <- colorRampPalette(colors = c("white","red"))
pheatmap(
mat = pmin( log( TPM_mat_names + 1 ) , 8 ) ,
cluster_rows = FALSE ,
cluster_cols = FALSE ,
scale = "none" ,
breaks = seq(0,8,length.out = 101) ,
color = scale_white_red(n = 100) ,
annotation_col = anno_col_rename ,
annotation_colors = list(
type = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1"),
age = c( young = "yellowgreen" , old = "slateblue")
),
gaps_col = 133,
show_colnames = FALSE,
fontsize_row = 7
)
Differentially expressed genes between old and young
DESeq2
library(DESeq2)Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: DelayedArray
Loading required package: matrixStats
Attaching package: ‘matrixStats’
The following object is masked from ‘package:dplyr’:
count
The following objects are masked from ‘package:Biobase’:
anyMissing, rowMedians
Attaching package: ‘DelayedArray’
The following objects are masked from ‘package:matrixStats’:
colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following object is masked from ‘package:base’:
applylibrary(tibble)
library(BiocParallel)Register number of cores for parallel execution
register(BPPARAM = MulticoreParam(workers = 4) , default = TRUE)Load the raw counts for all the cells
countData <- read.csv(file = "count_table/raw_counts_NSCs_SMARTseq2.csv" , header = TRUE , row.names = 1)And we load the cell annotation.
colData <- NSCs_annotation
colData$type_save <- colData$type
levels(colData$type) <- c("aNSC1","aNSC2","qNSC1","qNSC2")comparisons <- list( aNSC = c("aNSC1","aNSC2") , qNSC = c("qNSC1","qNSC2") , NSC = c("qNSC1","qNSC2","aNSC1","aNSC2") , qNSC1 = c("qNSC1") ,qNSC2 = c("qNSC2"), aNSC1 = c("aNSC1"), aNSC2 = c("aNSC2") )Now we want to see if all our cells available in the colData are also found in the countData table?
all(colData$cell %in% colnames(countData))[1] TRUEAnd if their order is the same?
all(colData$cell == colnames(countData)[-1])[1] TRUEGreat, thus we can proceed and set up our DESeq2 objects in a list.
The comparisons we are interested in are:
- aNSC: old vs young
- qNSC: old vs young
- all NSCs: old vs young
- individual celltypes: old vs young
Rename the gene column
countData <- dplyr::rename( countData , gene = ensembl_gene_id )Subset the data from colData and countData according to the celltypes into a list
celltype_data_list <- lapply(
X = comparisons ,
FUN = function(x){
colD <- dplyr::filter( colData , type %in% x )
cell_ids <- colD$cell
countD <- dplyr::select( countData , one_of( "gene" , as.character(cell_ids) ))
list( colData = colD , countData = countD )
}
)Check the order of the celltypes in the list
celltype_order <- lapply(
X = celltype_data_list ,
FUN = function(x){ as.character( unique( x$colData$type ) ) }
)
celltype_order$aNSC
[1] "aNSC1" "aNSC2"
$qNSC
[1] "qNSC1" "qNSC2"
$NSC
[1] "aNSC1" "aNSC2" "qNSC1" "qNSC2"
$qNSC1
[1] "qNSC1"
$qNSC2
[1] "qNSC2"
$aNSC1
[1] "aNSC1"
$aNSC2
[1] "aNSC2"Prepare DESeq objects
Make a list of DESeqDataSets - one for each celltype Only keep expressed genes in the data sets Set "young" as base level for comparison: Thus a FC > 0 will mean it is higher in the old cells
Because we have expected reads from the RSEM output, we need to round the values to integer, as DESeq2 expects integer values
celltype_data_list <- lapply(
X = celltype_data_list ,
FUN = function(x){
rownames(x$countData) <- NULL
rownames(x$colData) <- NULL
dds <- DESeqDataSetFromMatrix(countData = round(column_to_rownames( df = x$countData , var = "gene")),
colData = column_to_rownames( df = x$colData , var = "cell" ),
design = ~ age )
dds <- dds[ rowSums(counts(dds)) > 1, ]
dds$age <- relevel(x = dds$age , ref = "young")
dds
}
)Run DESeq
Run DESeq on the list of dds objects - will be parallely run on the number of cores set in the start of this file by register() ...
loopnum = 0
celltype_data_list <- lapply(
X = celltype_data_list ,
FUN = function(dds){
loopnum <<- loopnum + 1
print(paste("Running DESeq for field:", loopnum ) )
DESeq(object = dds , parallel = TRUE )
}
)Extract the results from the DESeq2 data sets
loopnum = 0
results_list <- lapply(
X = celltype_data_list ,
FUN = function(dds){
loopnum <<- loopnum + 1
print(paste("Extract results for field:", loopnum ) )
DESeq2::results(object = dds , tidy = TRUE , parallel = TRUE )
}
)Save the DESeq2 data sets list and the results list as RDS files
saveRDS(object = results_list , file = "results/DE_DESeq2_old_vs_young/results_list.RDS")
saveRDS(object = celltype_data_list , file = "results/DE_DESeq2_old_vs_young/DESeq2_celltype_data_list.RDS")Extract the individual DE tables from the list and save them individually as csv files
write.csv( x = results_list[[1]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[1]] , ".csv" ) )
write.csv( x = results_list[[2]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[2]] , ".csv" ) )
write.csv( x = results_list[[3]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[3]] , ".csv" ) )
write.csv( x = results_list[[4]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[4]] , ".csv" ) )
write.csv( x = results_list[[5]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[5]] , ".csv" ) )
write.csv( x = results_list[[6]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[6]] , ".csv" ) )
write.csv( x = results_list[[7]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[7]] , ".csv" ) )t-Test
NSCs
# Load the TPM expression values for the SMARTseq2 data
TPM_NSC <- remove_rownames(df = TPM_NSCs) %>% column_to_rownames(var = "ensembl_gene_id" )
# Load the cell annotation
NSC_anno <- NSCs_annotation
# Subset the expression matrix for young and old into different matrices
TPM_old <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "old",]$cell)]
TPM_young <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "young",]$cell)]
# Log transform the expression values
TPM_old <- log(TPM_old+1)
TPM_young <- log(TPM_young+1)
TPM_NSC <- as.matrix( log(TPM_NSC+1) )
# ------------------------------------------------------------------------------------------------------------
# Calculate the mean expression, standard deviation and number of cells with 0 counts for each row (gene) for young and old cells separately
table_sms2 <- data.frame(
ensembl_gene_id = rownames(TPM_young),
avg_logTPM_young = apply(TPM_young , MARGIN = 1 , FUN = mean) ,
avg_logTPM_old = apply(TPM_old , MARGIN = 1 , FUN = mean) ,
avg_logTPM = apply(TPM_NSC , MARGIN = 1 , FUN = mean) ,
sd_logTPM_young = apply(TPM_young , MARGIN = 1 , FUN = sd) ,
sd_logTPM_old = apply(TPM_old , MARGIN = 1 , FUN = sd),
fraction_cells_zero_young = apply(TPM_young == 0 , MARGIN = 1 , FUN = sum)/length(TPM_young),
fraction_cells_zero_old = apply(TPM_old == 0 , MARGIN = 1 , FUN = sum)/length(TPM_old)
)
# Calculate the difference between the mean from old and young (old - young)
table_sms2$difference_of_avg <- (table_sms2$avg_logTPM_old - table_sms2$avg_logTPM_young )
# Calculate the mean Standard Deviation between young and old
table_sms2$mean_sd <- apply( X = table_sms2[,c("sd_logTPM_young","sd_logTPM_old")] , MARGIN = 1 , FUN = mean)
# Finally calculate Cohens D, by deviding the difference of the mean values by the mean standard deviation
table_sms2$'difference_of_avg/mean_sd' <- table_sms2$difference_of_avg/table_sms2$mean_sd
# ------------------------------------------------------------------------------------------------------------
# Add p-values from t-test
# Run t-test on SMARTseq2 NSC data old and young
library(genefilter)
# Prepare factor with young and old in order of the column names
fact <- as.character( colnames(TPM_NSC) )
fact[ grepl(x = fact , pattern = "s_dot") ] <- "old"
fact[ grepl(x = fact , pattern = "^N" ) ] <- "young"
fact <- factor( fact )
# Prepare matrix for t-test input
TPM_NSC_mat <- TPM_NSC
TPM_NSC_mat <- as.matrix( TPM_NSC_mat )
# Run t-test with rowttests function from genefilter packages
t_test_NSCs_SMARTseq2 <- rowttests(x = TPM_NSC_mat , fac = fact )
# Add ensembl_gene_id as column from the rownames
t_test_NSCs_SMARTseq2$ensembl_gene_id <- rownames(t_test_NSCs_SMARTseq2)
# Join the tables
table_sms2_merged_with_ttest <- full_join(x = table_sms2 , y = t_test_NSCs_SMARTseq2 , by = "ensembl_gene_id" )Column `ensembl_gene_id` joining factor and character vector, coercing into character vectortable_sms2_merged_with_ttest <- arrange(table_sms2_merged_with_ttest , desc(difference_of_avg/mean_sd))
## Save the results as a csv file
# write.csv(x = table_sms2_merged_with_ttest , file = "DE_results/DE_NSC_SmartSeq2/DE_NSCs_SmartSeq2_t-test_CohensD.csv" )Expression of differentially expressed genes from Bulk NSCs
# Load bulk NSCs DE genes between old and young animals
bulk_DE_results <- read.csv("../Bulk_sequencing/DESeq2_results_old_vs_young/GP_DESeq2_results_Old_vs_Yang.csv" , row.names = 1)
# Merge the table of calculated values with the DESeq2 output of the bulk NSC comparison old vs young by ensembl_gene_id
tables_sms2_merged <- full_join(x = table_sms2 , y = bulk_DE_results)Joining, by = "ensembl_gene_id"
Column `ensembl_gene_id` joining factors with different levels, coercing to character vector# ------------------------------------------------------------------------------------------------------------
# Plot the difference in mean expression versus the parallel maximum of zero-count cell percentage
tables_sms2_merged %>%
filter(padj < 0.05) %>%
ggplot( aes(
x = pmax( fraction_cells_zero_old , fraction_cells_zero_young ) ,
y = avg_logTPM_old - avg_logTPM_young ,
color = factor(sign(log2FoldChange))
)) + geom_point()
# Same plot with blue (up) and red (down) colored points and axes labels
library(ggrepel)
library(cowplot)
as_tibble(tables_sms2_merged) %>%
filter( padj < 0.05) %>%
mutate( col = case_when(
padj > 0.05 ~ "n.s." ,
log2FoldChange > 0.3 ~ "up",
log2FoldChange < -0.3 ~ "down",
TRUE ~ "n.s." ) ) %>%
ggplot( aes(
x = pmax( fraction_cells_zero_old , fraction_cells_zero_young ) ,
y = avg_logTPM_old - avg_logTPM_young ,
color = col
)) +
geom_point() +
geom_point(size = 1) +
geom_hline(yintercept = 0 , color = "grey40") +
xlab("Max. percentage of cells with no reads per gene in old or young") +
ylab("Difference of avg. log(TPM+1) \n between old and young") +
theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) +
scale_color_manual( name = "Direction of change in bulk" , values = c(down = "red" , up = "blue")) 
# --> exported to PDF 6x9 inches and 4x9 (wide)individual subpopulations
# Load the TPM expression values for the SMARTseq2 data
TPM_NSC <- remove_rownames(df = TPM_NSCs) %>% column_to_rownames( var = "ensembl_gene_id" )
# Load the cell annotation
NSC_anno <- NSCs_annotation
NSC_anno$type <- recode_factor( NSC_anno$type , q1 = "qNSC1", q2 = "qNSC2", a1 = "aNSC1" , a2 = "aNSC2" )
# Subset the expression matrix for young and old into different matrices
TPM_old <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "old",]$cell)]
TPM_young <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "young",]$cell)]
# Log transform the expression values
TPM_old <- log(TPM_old+1)
TPM_young <- log(TPM_young+1)
TPM_NSC <- as.matrix( log(TPM_NSC+1) )
NSC_anno_young <- NSC_anno[NSC_anno$age == "young",]
NSC_anno_old <- NSC_anno[NSC_anno$age == "old",]
# ------------------------------------------------------------------------------------------------------------
# Function that perform calculation of
run_ttest_and_calculate_Cohens_d <- function(subpop){
## Subset cells for individual subpopulations
TPM_young_subpop <- TPM_young[as.character(NSC_anno_young[NSC_anno_young$type == subpop,]$cell)]
TPM_old_subpop <- TPM_old[as.character(NSC_anno_old[NSC_anno_old$type == subpop,]$cell)]
TPM_NSC_subpop <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == subpop,]$cell)]
# Calculate the mean expression, standard deviation and number of cells with 0 counts for each row (gene) for young and old cells separately
table_sms2 <- data.frame(
ensembl_gene_id = rownames(TPM_young_subpop),
avg_logTPM_young = apply(TPM_young_subpop , MARGIN = 1 , FUN = mean) ,
avg_logTPM_old = apply(TPM_old_subpop , MARGIN = 1 , FUN = mean) ,
sd_logTPM_young = apply(TPM_young_subpop , MARGIN = 1 , FUN = sd) ,
sd_logTPM_old = apply(TPM_old_subpop , MARGIN = 1 , FUN = sd),
fraction_cells_zero_young = apply(TPM_young_subpop == 0 , MARGIN = 1 , FUN = sum)/length(TPM_young_subpop),
fraction_cells_zero_old = apply(TPM_old_subpop == 0 , MARGIN = 1 , FUN = sum)/length(TPM_old_subpop)
)
# Calculate the difference between the mean from old and young (old - young)
table_sms2$difference_of_avg <- (table_sms2$avg_logTPM_old - table_sms2$avg_logTPM_young )
# Calculate the mean Standard Deviation between young and old
table_sms2$mean_sd <- apply( X = table_sms2[,c("sd_logTPM_young","sd_logTPM_old")] , MARGIN = 1 , FUN = mean)
# Finally calculate Cohens D, by deviding the difference of the mean values by the mean standard deviation
table_sms2$'difference_of_avg/mean_sd' <- table_sms2$difference_of_avg/table_sms2$mean_sd
# ------------------------------------------------------------------------------------------------------------
# Add p-values from t-test
# Run t-test on SMARTseq2 NSC data old and young
# Prepare factor with young and old in order of the column names
fact <- as.character( colnames(TPM_NSC_subpop) )
fact[ grepl(x = fact , pattern = "s_dot") ] <- "old"
fact[ grepl(x = fact , pattern = "^N" ) ] <- "young"
fact <- factor( fact )
# Prepare matrix for t-test input
TPM_NSC_mat <- TPM_NSC_subpop
TPM_NSC_mat <- as.matrix( TPM_NSC_mat )
# Run t-test with rowttests function from genefilter packages
t_test_NSCs_SMARTseq2 <- rowttests(x = TPM_NSC_mat , fac = fact )
# Add ensembl_gene_id as column from the rownames
t_test_NSCs_SMARTseq2$ensembl_gene_id <- rownames(t_test_NSCs_SMARTseq2)
# Join the tables
table_sms2_merged_with_ttest <- full_join(x = table_sms2 , y = t_test_NSCs_SMARTseq2 , by = "ensembl_gene_id" )
table_sms2_merged_with_ttest
}
# ------------------------------------------------------------------------------------------------------------
# Run the t-test for all subpopulations individually
subpopulations <- list(qNSC1 = "qNSC1", qNSC2 = "qNSC2", aNSC1 = "aNSC1", aNSC2 = "aNSC2")
ttest_results_cohensd <- lapply(X = subpopulations , FUN = run_ttest_and_calculate_Cohens_d ) Column `ensembl_gene_id` joining factor and character vector, coercing into character vectorColumn `ensembl_gene_id` joining factor and character vector, coercing into character vectorColumn `ensembl_gene_id` joining factor and character vector, coercing into character vectorColumn `ensembl_gene_id` joining factor and character vector, coercing into character vector# Order the table by cohens d
ttest_results_cohensd <- lapply(ttest_results_cohensd, FUN = function(x){arrange(x, desc(difference_of_avg/mean_sd))} )
## Save the results as a csv file
# write.csv(x = ttest_results_cohensd[[1]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[1]],".csv") )
# write.csv(x = ttest_results_cohensd[[2]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[2]],".csv") )
# write.csv(x = ttest_results_cohensd[[3]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[3]],".csv") )
# write.csv(x = ttest_results_cohensd[[4]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[4]],".csv") )Difference of the mean Expression vs. Mean SD Plots
qNSC1 vs. qNSC2
## t-test between qNSC1 and qNSC2 SMARTseq2
## Subset cells for individual subpopulations
TPM_NSC_qNSC1 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "qNSC1",]$cell)]
TPM_NSC_qNSC2 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "qNSC2",]$cell)]
TPM_NSC_aNSC1 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "aNSC1",]$cell)]
TPM_NSC_aNSC2 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "aNSC2",]$cell)]
TPM_NSC_qNSC1_qNSC2 <- as.matrix( cbind( TPM_NSC_qNSC1 , TPM_NSC_qNSC2 ) )
q1q2 <- c( rep( "qNSC1" , 134 ) , rep( "qNSC2" , 40 ) )
t_test_qNSC1_vs_2_SMARTseq2 <- rowttests(x = as.matrix( TPM_NSC_qNSC1_qNSC2 ) , fac = factor( q1q2 ) )
tab <- data.frame(
ensembl_gene_id = rownames(TPM_NSC_qNSC1),
mean_qNSC1 = rowMeans( TPM_NSC_qNSC1 ) ,
mean_qNSC2 = rowMeans( TPM_NSC_qNSC2 ) ,
var_qNSC1 = apply(X = TPM_NSC_qNSC1 , MARGIN = 1 , FUN = var ),
var_qNSC2 = apply(X = TPM_NSC_qNSC2 , MARGIN = 1 , FUN = var ),
sd_qNSC1 = apply(X = TPM_NSC_qNSC1 , MARGIN = 1 , FUN = sd ),
sd_qNSC2 = apply(X = TPM_NSC_qNSC2 , MARGIN = 1 , FUN = sd )
)
tab$mean_var <- apply(X = tab[,c("var_qNSC1","var_qNSC2")] , MARGIN = 1 , FUN = mean )
tab$mean_sd <- apply(X = tab[,c("sd_qNSC1","sd_qNSC2")] , MARGIN = 1 , FUN = mean )
tab$difference_of_avg <- (tab$mean_qNSC1 - tab$mean_qNSC2 )
tab$'difference_of_avg/mean_sd' <- tab$difference_of_avg/tab$mean_sd
# Add ensembl_gene_id as column from the rownames
t_test_qNSC1_vs_2_SMARTseq2$ensembl_gene_id <- rownames(t_test_qNSC1_vs_2_SMARTseq2)
# Join the tables
t_test_qNSC1_vs_2_SMARTseq2_merged_with_ttest <- full_join(x = tab , y = t_test_qNSC1_vs_2_SMARTseq2 , by = "ensembl_gene_id" )Column `ensembl_gene_id` joining factor and character vector, coercing into character vectorlibrary(cowplot)
ggplot(data = t_test_qNSC1_vs_2_SMARTseq2_merged_with_ttest , aes(y = mean_sd , x = mean_qNSC1 - mean_qNSC2 , color = abs(difference_of_avg/mean_sd) > 0.8 )) + geom_point( alpha = 0.5 , size = 1 ) + ggtitle("Comparison: qNSC1 vs qNSC2") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) + xlab("Difference of mean expressions") + ylab("Average SD")
ggplot(data = tab , aes(y = mean_sd , x = mean_qNSC1 - mean_qNSC2 )) + geom_point( alpha = 0.1 , size = 1 ) + ggtitle("Comparison: qNSC1 vs qNSC2") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) + xlab("Difference of mean expressions") + ylab("Average SD")
qNSC1: old vs. young
tab_q1_old_young <- run_ttest_and_calculate_Cohens_d(subpop = "qNSC1" )Column `ensembl_gene_id` joining factor and character vector, coercing into character vectorggplot(data = tab_q1_old_young , aes(y = mean_sd , x = difference_of_avg , color = abs(difference_of_avg/mean_sd) > 0.8 )) + geom_point( alpha = 0.5 , size = 1 ) + ggtitle("Comparison: qNSC1 - old vs young") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) + xlab("Difference of mean expressions") + ylab("Average SD") 
ggplot(data = tab_q1_old_young , aes(y = mean_sd , x = difference_of_avg )) + geom_point( alpha = 0.1 , size = 1 ) + ggtitle("Comparison: qNSC1 - old vs young") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) + xlab("Difference of mean expressions") + ylab("Average SD") 
Euclidean Distances (work on it)
Load the TPM values
smartseq2_data <- read.csv(file = "count_table/TPM_NSC_and_Astro/old_young_combined/Gene_expression_matrix_TPM_all.csv" , row.names = 1)
smartseq2_data <- smartseq2_data %>% remove_rownames() %>% column_to_rownames("gene_id")Also we load the annotation
smartseq2_annotation <- read.csv(file = "count_table/cell_annotation_NSC_Astro/old_young_combined/cell_annotation.csv" , row.names = 1)Which celltypes do we have?
celltypes <- unique(as.character(smartseq2_annotation$type))
celltypes[1] "Astro" "aNSC1" "aNSC2" "qNSC1" "qNSC2"Create Seurat object
seurat_sms2 <- CreateSeuratObject(raw.data = smartseq2_data , min.cells = 3, project = "young_vs_old_SmartSeq2")
seurat_sms2 <- AddMetaData(object = seurat_sms2, metadata = smartseq2_annotation %>% remove_rownames() %>% column_to_rownames("cell") )
seurat_sms2 <- NormalizeData(object = seurat_sms2, normalization.method = "LogNormalize" )Performing log-normalization
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|seurat_sms2 <- ScaleData(object = seurat_sms2, vars.to.regress = c("nUMI", "nGene"))[1] "Regressing out nUMI" "Regressing out nGene"
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|==================================================================================================================== | 99%
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|=====================================================================================================================| 100%
[1] "Scaling data matrix"
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|=====================================================================================================================| 100%seurat_sms2 <- FindVariableGenes(object = seurat_sms2, mean.function = ExpMean, dispersion.function = LogVMR, y.cutoff = 0.75)Calculating gene means
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**************************************************|
Calculating gene variance to mean ratios
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genes.var <- apply(X = seurat_sms2@raw.data , MARGIN = 1 , FUN = var)
genes.var.top <- names( sort(genes.var , decreasing = TRUE)[1:2000] )
seurat_sms2@var.genes <- genes.var.topseurat_sms2@imputed <- as.data.frame.matrix(seurat_sms2@data)seurat_sms2 <- RunPCA(object = seurat_sms2, pc.genes = seurat_sms2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5 , use.imputed = TRUE )[1] "PC1"
[1] "ENSMUSG00000047945" "ENSMUSG00000020737" "ENSMUSG00000049775" "ENSMUSG00000001525" "ENSMUSG00000003038"
[1] ""
[1] "ENSMUSG00000028517" "ENSMUSG00000022037" "ENSMUSG00000006205" "ENSMUSG00000002985" "ENSMUSG00000041329"
[1] ""
[1] ""
[1] "PC2"
[1] "ENSMUSG00000026701" "ENSMUSG00000026385" "ENSMUSG00000001270" "ENSMUSG00000001025" "ENSMUSG00000053398"
[1] ""
[1] "ENSMUSG00000034120" "ENSMUSG00000061331" "ENSMUSG00000100131" "ENSMUSG00000100862" "ENSMUSG00000038943"
[1] ""
[1] ""
[1] "PC3"
[1] "ENSMUSG00000010095" "ENSMUSG00000039542" "ENSMUSG00000027562" "ENSMUSG00000058927" "ENSMUSG00000030317"
[1] ""
[1] "ENSMUSG00000021702" "ENSMUSG00000037852" "ENSMUSG00000026728" "ENSMUSG00000001025" "ENSMUSG00000023010"
[1] ""
[1] ""
[1] "PC4"
[1] "ENSMUSG00000004902" "ENSMUSG00000039323" "ENSMUSG00000097971" "ENSMUSG00000035202" "ENSMUSG00000061331"
[1] ""
[1] "ENSMUSG00000030654" "ENSMUSG00000041577" "ENSMUSG00000030905" "ENSMUSG00000027496" "ENSMUSG00000030867"
[1] ""
[1] ""
[1] "PC5"
[1] "ENSMUSG00000021750" "ENSMUSG00000031762" "ENSMUSG00000057666" "ENSMUSG00000004610" "ENSMUSG00000053931"
[1] ""
[1] "ENSMUSG00000020571" "ENSMUSG00000027248" "ENSMUSG00000030342" "ENSMUSG00000027195" "ENSMUSG00000032046"
[1] ""
[1] ""seurat_sms2 <- ProjectPCA(object = seurat_sms2 )[1] "PC1"
[1] "ENSMUSG00000094627" "ENSMUSG00000047945" "ENSMUSG00000003038" "ENSMUSG00000000184" "ENSMUSG00000049775"
[6] "ENSMUSG00000070713" "ENSMUSG00000020737" "ENSMUSG00000026238" "ENSMUSG00000094790" "ENSMUSG00000001525"
[11] "ENSMUSG00000028832" "ENSMUSG00000046434" "ENSMUSG00000079523" "ENSMUSG00000045128" "ENSMUSG00000004530"
[16] "ENSMUSG00000067274" "ENSMUSG00000032518" "ENSMUSG00000015217" "ENSMUSG00000028639" "ENSMUSG00000030744"
[21] "ENSMUSG00000054717" "ENSMUSG00000025362" "ENSMUSG00000096544" "ENSMUSG00000060143" "ENSMUSG00000098318"
[26] "ENSMUSG00000096006" "ENSMUSG00000057841" "ENSMUSG00000029430" "ENSMUSG00000040952" "ENSMUSG00000032399"
[1] ""
[1] "ENSMUSG00000006205" "ENSMUSG00000028517" "ENSMUSG00000026424" "ENSMUSG00000041329" "ENSMUSG00000045092"
[6] "ENSMUSG00000050953" "ENSMUSG00000022037" "ENSMUSG00000004892" "ENSMUSG00000007097" "ENSMUSG00000005360"
[11] "ENSMUSG00000017390" "ENSMUSG00000002985" "ENSMUSG00000020591" "ENSMUSG00000029309" "ENSMUSG00000079037"
[16] "ENSMUSG00000058254" "ENSMUSG00000022564" "ENSMUSG00000005089" "ENSMUSG00000030495" "ENSMUSG00000022132"
[21] "ENSMUSG00000102349" "ENSMUSG00000030310" "ENSMUSG00000028128" "ENSMUSG00000027447" "ENSMUSG00000092341"
[26] "ENSMUSG00000020333" "ENSMUSG00000021508" "ENSMUSG00000030428" "ENSMUSG00000031760" "ENSMUSG00000031517"
[1] ""
[1] ""
[1] "PC2"
[1] "ENSMUSG00000026701" "ENSMUSG00000037852" "ENSMUSG00000030246" "ENSMUSG00000058135" "ENSMUSG00000001270"
[6] "ENSMUSG00000026385" "ENSMUSG00000053398" "ENSMUSG00000001025" "ENSMUSG00000018451" "ENSMUSG00000018567"
[11] "ENSMUSG00000024661" "ENSMUSG00000030695" "ENSMUSG00000044080" "ENSMUSG00000027523" "ENSMUSG00000029455"
[16] "ENSMUSG00000040997" "ENSMUSG00000074457" "ENSMUSG00000004558" "ENSMUSG00000032294" "ENSMUSG00000017390"
[21] "ENSMUSG00000033059" "ENSMUSG00000030934" "ENSMUSG00000053931" "ENSMUSG00000024425" "ENSMUSG00000055254"
[26] "ENSMUSG00000029446" "ENSMUSG00000068523" "ENSMUSG00000076441" "ENSMUSG00000024646" "ENSMUSG00000022108"
[1] ""
[1] "ENSMUSG00000042501" "ENSMUSG00000070495" "ENSMUSG00000102443" "ENSMUSG00000075307" "ENSMUSG00000086607"
[6] "ENSMUSG00000097452" "ENSMUSG00000002297" "ENSMUSG00000085635" "ENSMUSG00000100768" "ENSMUSG00000050097"
[11] "ENSMUSG00000093684" "ENSMUSG00000092814" "ENSMUSG00000101776" "ENSMUSG00000020914" "ENSMUSG00000096111"
[16] "ENSMUSG00000104329" "ENSMUSG00000036109" "ENSMUSG00000056888" "ENSMUSG00000096061" "ENSMUSG00000103234"
[21] "ENSMUSG00000104060" "ENSMUSG00000021998" "ENSMUSG00000069892" "ENSMUSG00000038943" "ENSMUSG00000036815"
[26] "ENSMUSG00000019942" "ENSMUSG00000061331" "ENSMUSG00000097695" "ENSMUSG00000102858" "ENSMUSG00000104377"
[1] ""
[1] ""
[1] "PC3"
[1] "ENSMUSG00000021508" "ENSMUSG00000006205" "ENSMUSG00000036949" "ENSMUSG00000027562" "ENSMUSG00000033998"
[6] "ENSMUSG00000030317" "ENSMUSG00000039542" "ENSMUSG00000026424" "ENSMUSG00000063524" "ENSMUSG00000032281"
[11] "ENSMUSG00000030235" "ENSMUSG00000010095" "ENSMUSG00000002475" "ENSMUSG00000049612" "ENSMUSG00000040055"
[16] "ENSMUSG00000020644" "ENSMUSG00000030310" "ENSMUSG00000028128" "ENSMUSG00000035237" "ENSMUSG00000003974"
[21] "ENSMUSG00000066026" "ENSMUSG00000031467" "ENSMUSG00000032014" "ENSMUSG00000001260" "ENSMUSG00000033208"
[26] "ENSMUSG00000030495" "ENSMUSG00000032349" "ENSMUSG00000043496" "ENSMUSG00000031604" "ENSMUSG00000020614"
[1] ""
[1] "ENSMUSG00000026728" "ENSMUSG00000026185" "ENSMUSG00000034892" "ENSMUSG00000028234" "ENSMUSG00000031548"
[6] "ENSMUSG00000021702" "ENSMUSG00000021087" "ENSMUSG00000050621" "ENSMUSG00000007892" "ENSMUSG00000045128"
[11] "ENSMUSG00000046364" "ENSMUSG00000093674" "ENSMUSG00000023169" "ENSMUSG00000032518" "ENSMUSG00000025362"
[16] "ENSMUSG00000071415" "ENSMUSG00000090733" "ENSMUSG00000070498" "ENSMUSG00000019539" "ENSMUSG00000037805"
[21] "ENSMUSG00000060019" "ENSMUSG00000083219" "ENSMUSG00000041841" "ENSMUSG00000067288" "ENSMUSG00000046330"
[26] "ENSMUSG00000039001" "ENSMUSG00000049775" "ENSMUSG00000001025" "ENSMUSG00000037852" "ENSMUSG00000057322"
[1] ""
[1] ""
[1] "PC4"
[1] "ENSMUSG00000103234" "ENSMUSG00000004902" "ENSMUSG00000100768" "ENSMUSG00000101776" "ENSMUSG00000039323"
[6] "ENSMUSG00000097695" "ENSMUSG00000097551" "ENSMUSG00000101749" "ENSMUSG00000102443" "ENSMUSG00000083594"
[11] "ENSMUSG00000102858" "ENSMUSG00000061331" "ENSMUSG00000035202" "ENSMUSG00000033981" "ENSMUSG00000063730"
[16] "ENSMUSG00000048148" "ENSMUSG00000104060" "ENSMUSG00000085328" "ENSMUSG00000087267" "ENSMUSG00000103322"
[21] "ENSMUSG00000087306" "ENSMUSG00000042501" "ENSMUSG00000097156" "ENSMUSG00000104329" "ENSMUSG00000035694"
[26] "ENSMUSG00000044519" "ENSMUSG00000101111" "ENSMUSG00000094472" "ENSMUSG00000078808" "ENSMUSG00000104377"
[1] ""
[1] "ENSMUSG00000019942" "ENSMUSG00000023505" "ENSMUSG00000005233" "ENSMUSG00000027715" "ENSMUSG00000012443"
[6] "ENSMUSG00000028873" "ENSMUSG00000024056" "ENSMUSG00000023015" "ENSMUSG00000001403" "ENSMUSG00000030677"
[11] "ENSMUSG00000027469" "ENSMUSG00000022033" "ENSMUSG00000020897" "ENSMUSG00000020914" "ENSMUSG00000034906"
[16] "ENSMUSG00000048327" "ENSMUSG00000035683" "ENSMUSG00000048922" "ENSMUSG00000038943" "ENSMUSG00000030867"
[21] "ENSMUSG00000003779" "ENSMUSG00000017716" "ENSMUSG00000027496" "ENSMUSG00000041431" "ENSMUSG00000062248"
[26] "ENSMUSG00000027306" "ENSMUSG00000026683" "ENSMUSG00000020808" "ENSMUSG00000038379" "ENSMUSG00000040084"
[1] ""
[1] ""
[1] "PC5"
[1] "ENSMUSG00000019942" "ENSMUSG00000027715" "ENSMUSG00000005233" "ENSMUSG00000001403" "ENSMUSG00000003779"
[6] "ENSMUSG00000028873" "ENSMUSG00000020914" "ENSMUSG00000048327" "ENSMUSG00000017716" "ENSMUSG00000027469"
[11] "ENSMUSG00000023015" "ENSMUSG00000030867" "ENSMUSG00000012443" "ENSMUSG00000030677" "ENSMUSG00000020808"
[16] "ENSMUSG00000020897" "ENSMUSG00000062248" "ENSMUSG00000038943" "ENSMUSG00000041431" "ENSMUSG00000048922"
[21] "ENSMUSG00000024056" "ENSMUSG00000029910" "ENSMUSG00000035683" "ENSMUSG00000023505" "ENSMUSG00000039396"
[26] "ENSMUSG00000022033" "ENSMUSG00000026683" "ENSMUSG00000034906" "ENSMUSG00000027306" "ENSMUSG00000006398"
[1] ""
[1] "ENSMUSG00000027248" "ENSMUSG00000020571" "ENSMUSG00000007891" "ENSMUSG00000032046" "ENSMUSG00000021094"
[6] "ENSMUSG00000029838" "ENSMUSG00000030342" "ENSMUSG00000025351" "ENSMUSG00000027195" "ENSMUSG00000030605"
[11] "ENSMUSG00000032324" "ENSMUSG00000020591" "ENSMUSG00000019818" "ENSMUSG00000037089" "ENSMUSG00000008140"
[16] "ENSMUSG00000015575" "ENSMUSG00000037706" "ENSMUSG00000031447" "ENSMUSG00000030062" "ENSMUSG00000022382"
[21] "ENSMUSG00000020570" "ENSMUSG00000033629" "ENSMUSG00000022108" "ENSMUSG00000032383" "ENSMUSG00000024150"
[26] "ENSMUSG00000037720" "ENSMUSG00000026223" "ENSMUSG00000030286" "ENSMUSG00000024121" "ENSMUSG00000029778"
[1] ""
[1] ""seurat_sms2 <- JackStraw(object = seurat_sms2 )PCElbowPlot(object = seurat_sms2)
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type")PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_sms2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = SetAllIdent( seurat_sms2 , id = "age" ) , dim.1 = 1, dim.2 = 4)
Log transform the TPM values
smartseq2_data <- log(1+smartseq2_data)Make a matrix of the data
data_matrix_celltypes <- lapply( X = celltypes, FUN = function(x){
cells <- filter(smartseq2_annotation , type == x) %>% pull(cell) %>% as.character()
# t(as.matrix(smartseq2_data[,cells]))
FetchData(object = seurat_sms2 , vars.all = c("PC1","PC2","PC3","PC4","PC5") , cells.use = cells )
})Now we calculate the euclidean distance between all the cells from one celltype
euc_dist_celltypes <- lapply( data_matrix_celltypes , FUN = function(x){as.matrix(dist(x = x, method = "euclidean" , upper = TRUE))} )pheatmap(
main = celltypes[[1]],
mat = euc_dist_celltypes[[1]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = celltypes[[2]],
mat = euc_dist_celltypes[[2]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = celltypes[[3]],
mat = euc_dist_celltypes[[3]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = celltypes[[4]],
mat = euc_dist_celltypes[[4]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = celltypes[[5]],
mat = euc_dist_celltypes[[5]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
Compare all samples to each other
rownames(smartseq2_annotation) <- NULL
anno <- smartseq2_annotation %>% mutate(type = factor(type , levels = c("qNSC1","qNSC2","aNSC1","aNSC2","Astro"))) %>% arrange(age , type) %>% column_to_rownames( var = "cell")dist_all_samples <- as.matrix(dist(x = t(as.matrix(smartseq2_data)[,rownames(anno)]), method = "euclidean" , upper = TRUE))pheatmap(
main = "Euclidean Distance between all samples",
mat = dist_all_samples,
cluster_rows = FALSE,
cluster_cols = FALSE,
annotation_row = anno,
annotation_col = anno,
annotation_colors = list( type = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1", Astro = "grey") , age = c(young = "forestgreen" , old = "red")) ,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
Density of euclidean distances
df.plot.list <- lapply( X = euc_dist_celltypes, FUN = function(x){
x %>% as.data.frame() %>% rownames_to_column("cell1") %>% gather(key = "cell2" , value = "euclidean_distance" , -cell1) %>% left_join( y = dplyr::rename(anno, age_cell1 = age) %>% rownames_to_column( var = "cell1") , by = "cell1" ) %>% left_join( y = dplyr::rename(anno, age_cell2 = age) %>% rownames_to_column( var = "cell2") , by = "cell2" ) %>% mutate(comparison = paste(age_cell1 , age_cell2 , sep = "_")) %>% dplyr::filter(comparison %in% c("old_old","young_old","young_young"))
})gg.list <- lapply( X = df.plot.list, FUN = function(x){
ggplot(data = x, mapping = aes(x = euclidean_distance , color = comparison , fill = comparison )) + geom_density( alpha = 0.3) + ggtitle( paste0(unique(as.character(x$type.x))) )
})Astro
print(gg.list[[1]])
aNSC1
print(gg.list[[2]])
aNSC2
print(gg.list[[3]])
qNSC1
print(gg.list[[4]])
qNSC2
print(gg.list[[5]])
Euclidean Distances between celltypes
celltype_comparisons <- list(Astro_qNSC1 = c("Astro","qNSC1") , qNSC1_qNSC2 = c("qNSC1","qNSC2") , qNSC2_aNSC1 = c("qNSC2","aNSC1") , aNSC1_aNSC2 = c("aNSC1","aNSC2") , qNSC1_aNSC2 = c("qNSC1","aNSC2") )
celltype_comparisons$Astro_qNSC1
[1] "Astro" "qNSC1"
$qNSC1_qNSC2
[1] "qNSC1" "qNSC2"
$qNSC2_aNSC1
[1] "qNSC2" "aNSC1"
$aNSC1_aNSC2
[1] "aNSC1" "aNSC2"
$qNSC1_aNSC2
[1] "qNSC1" "aNSC2"Make a matrix of the data
data_matrix_lineage <- lapply( X = celltype_comparisons, FUN = function(x){
cells <- filter(smartseq2_annotation , type %in% x) %>% pull(cell) %>% as.character()
# t(as.matrix(smartseq2_data[,cells]))
FetchData(object = seurat_sms2 , vars.all = c("PC1","PC2","PC3","PC4","PC5") , cells.use = cells )
})Now we calculate the euclidean distance between all the cells from the comparison
euc_dist_lineage_all <- lapply( data_matrix_lineage , FUN = function(x){as.matrix(dist(x = x, method = "euclidean" , upper = TRUE))} )pheatmap(
main = paste(celltype_comparisons[[1]], collapse = "_vs_"),
mat = euc_dist_lineage_all[[1]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = paste(celltype_comparisons[[2]], collapse = "_vs_"),
mat = euc_dist_lineage_all[[2]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = paste(celltype_comparisons[[3]], collapse = "_vs_"),
mat = euc_dist_lineage_all[[3]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = paste(celltype_comparisons[[4]], collapse = "_vs_"),
mat = euc_dist_lineage_all[[4]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
pheatmap(
main = paste(celltype_comparisons[[5]], collapse = "_vs_"),
mat = euc_dist_lineage_all[[5]],
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = TRUE ,
show_colnames = TRUE ,
display_numbers = FALSE ,
number_color = "black" ,
fontsize = 14
)
Density of euclidean distances
df.plot.list_all <- lapply( X = euc_dist_lineage_all, FUN = function(x){
x %>% as.data.frame() %>% rownames_to_column("cell1") %>% gather(key = "cell2" , value = "euclidean_distance" , -cell1) %>% left_join( y = dplyr::rename(anno, type_cell1 = type) %>% rownames_to_column( var = "cell1") , by = "cell1" ) %>% left_join( y = dplyr::rename(anno, type_cell2 = type) %>% rownames_to_column( var = "cell2") , by = "cell2" ) %>% mutate(comparison = paste(type_cell1 , type_cell2 , sep = "_"))
})gg.list_all <- lapply( X = df.plot.list_all, FUN = function(x){
ggplot(data = x, mapping = aes(x = euclidean_distance , color = comparison)) + geom_density() + ggtitle( "" )
})print(gg.list_all[[1]])
print(gg.list_all[[2]])
print(gg.list_all[[3]])
print(gg.list_all[[4]])
print(gg.list_all[[5]])
Euclidean distances young vs old
q1_q2_euc_dist <- df.plot.list_all$qNSC1_qNSC2 %>% filter(comparison == "qNSC1_qNSC2")df.plot.list_ref <- lapply( X = euc_dist_celltypes, FUN = function(x){
x %>% as.data.frame() %>% rownames_to_column("cell1") %>% gather(key = "cell2" , value = "euclidean_distance" , -cell1) %>% left_join( y = dplyr::rename(anno, age_cell1 = age) %>% rownames_to_column( var = "cell1") , by = "cell1" ) %>% left_join( y = dplyr::rename(anno, age_cell2 = age) %>% rownames_to_column( var = "cell2") , by = "cell2" ) %>% mutate(comparison = paste(age_cell1 , age_cell2 , sep = "_")) %>% dplyr::mutate(type = as.character(type.x) ) %>% dplyr::select( cell1,cell2,euclidean_distance,comparison , type) %>% bind_rows(q1_q2_euc_dist %>% dplyr::mutate(type = as.character(type_cell1) ) %>% dplyr::select( cell1,cell2,euclidean_distance,comparison,type)) %>% dplyr::filter(comparison %in% c("old_old","young_old","young_young","qNSC1_qNSC2")) %>% mutate(comparison = factor(comparison , levels = c("old_old","young_old","young_young","qNSC1_qNSC2")))
})colors_comparisons <- c("old_old" = "slateblue" ,"young_old" = "deepskyblue4", "young_young" = "olivedrab" , "qNSC1_qNSC2" = "black")
gg.list <- lapply( X = df.plot.list_ref, FUN = function(x){
ggplot(data = x, mapping = aes(x = euclidean_distance , color = comparison , fill = comparison )) + geom_density( alpha = 0.3) + ggtitle( paste0(unique(as.character(x$type))) ) + xlab("Euclidean Distance") + ylab("Density") + scale_fill_manual(values = colors_comparisons) + scale_color_manual( values = colors_comparisons ) + labs(color="Comparison",fill="Comparison") + ggtitle( paste0(unique(as.character(x$type))) )
})Astro
print(gg.list[[1]])
aNSC1
print(gg.list[[2]])
aNSC2
print(gg.list[[3]])
qNSC1
print(gg.list[[4]])
qNSC2
print(gg.list[[5]])
qNSC1 and Astrocytes
BoxPlotSmart <- function( x , anno , idents , genes , trans_log2 = TRUE , pt.alpha = 1 , facet_switch = FALSE ){
require(dplyr)
require(tidyr)
require(ggplot2)
require(ggbeeswarm)
require(cowplot)
require(hash)
ens2gene_hash <- hash( keys = genes , values = names(genes) )
cells <- as.character( filter( anno , type %in% idents ) %>% pull("cell") )
data_violin <- dplyr::select(x , one_of( c( "gene_id" , cells ) ) ) %>% filter( gene_id %in% genes )
data_violin_gather <- gather( data = data_violin , key = "cell" , value = "TPM" , -gene_id )
if(trans_log2){ data_violin_gather$TPM <- log2( data_violin_gather$TPM + 1 ) }
data_violin_gather_joined <- left_join(x = data_violin_gather , y = anno, by = "cell")
print( data_violin_gather_joined %>% group_by(gene_id,type) %>% summarise( percent = sum(TPM > 0)/length(TPM) ) , gene_id = gene_id )
gene_ids <- hash::values(ens2gene_hash[genes])
gene_ids <- gene_ids[! is.na(gene_ids)]
genes_labeller_vec <- gene_ids
genes_labeller <- labeller(gene_id = genes_labeller_vec)
data_violin_gather_joined <- dplyr::filter(data_violin_gather_joined , gene_id %in% names(genes_labeller_vec) )
g <- ggplot(data = data_violin_gather_joined , mapping = aes(x = type , y = TPM , fill = type )) +
geom_boxplot( outlier.shape = NA ) +
geom_quasirandom(alpha = pt.alpha , color = "black" , size = 0.7 ) +
scale_x_discrete( name = "Identity") +
scale_y_continuous(name = if(trans_log2){"log2( TPM + 1 )"}else{"TPM"}) +
scale_fill_manual(name = "Identity" , values = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , Astro = "grey50" ) ) +
if( all( c("old","young") %in% as.character(anno$age) ) ){
if(! facet_switch){
facet_grid(facets = gene_id~age , labeller = genes_labeller)
}else{
facet_grid(facets = age~gene_id , labeller = genes_labeller)
}
}else{
facet_wrap( facets = "gene_id" , ncol = 1 , labeller = genes_labeller )
}
return(g)
}Load the data
Load the SmartSeq2 data for young and old separately
smartseq_data_old <- read.csv(file = "count_table/TPM_NSC_and_Astro/old/Gene_expression_matrix_TPM_old.csv" , header = TRUE, row.names = 1)
smartseq_data_young <- read.csv(file = "count_table/TPM_NSC_and_Astro/young/Gene_expression_matrix_TPM_young.csv" , header = TRUE, row.names = 1)Join young and old
# smartseq_data <- full_join(x = smartseq_data_old , y = smartseq_data_young)and the cell annotation to celltypes
smartseq_data_annotation_old <- read.csv(file = "count_table/cell_annotation_NSC_Astro/old/cell_annotation_old.csv" , header = TRUE, row.names = 1)
smartseq_data_annotation_young <- read.csv(file = "count_table/cell_annotation_NSC_Astro/young/cell_annotation_young.csv" , header = TRUE, row.names = 1)Join young and old
# smartseq_data_annotation <- bind_rows( smartseq_data_annotation_old, smartseq_data_annotation_young )Boxplot Cd9
BoxPlotSmart(x = smartseq_data_old , anno = smartseq_data_annotation_old , idents = c("qNSC1","Astro") , genes = c(Cd9 = "ENSMUSG00000030342") )Column `cell` joining character vector and factor, coercing into character vector
cd9plot_old <- BoxPlotSmart(x = smartseq_data_old , anno = smartseq_data_annotation_old , idents = c("qNSC1","Astro") , genes = c(Cd9 = "ENSMUSG00000030342") , pt.alpha = 1) + theme(legend.position = "none") Column `cell` joining character vector and factor, coercing into character vectorcd9plot_old
cd9plot_young <- BoxPlotSmart(x = smartseq_data_young , anno = smartseq_data_annotation_young , idents = c("qNSC1","Astro") , genes = c(Cd9 = "ENSMUSG00000030342") , pt.alpha = 1) + theme(legend.position = "none") Column `cell` joining character vector and factor, coercing into character vectorcd9plot_young
Heatmap differentially expressed genes
Load the lists of differentially expressed genes between cortical Astrocytes and qNSC1s, for cells from old mice.
# Old
DEgenes_old_tab <- read.csv(file = "results/q1_old_vs_q1_oldAstro_sig_results.csv")
DEgenes_old_tab$gene_name <- str_to_title(DEgenes_old_tab$gene_name)
ens2gene_old <- DEgenes_old_tab
DEgenes_old_up <- dplyr::filter(DEgenes_old_tab , log_fold_chang > 0 , ! is.na(gene_id))
DEgenes_old_down <- dplyr::filter(DEgenes_old_tab , log_fold_chang < 0 , ! is.na(gene_id))
seurat_10X2 <- readRDS(file = "../10X/seurat_10X2_clustered_min_1500_nGene.RDS")
DEgenes_old_up_comp <- dplyr::filter(DEgenes_old_up , gene_name %in% rownames(seurat_10X2@data) )
DEgenes_old_down_comp <- dplyr::filter(DEgenes_old_down , gene_name %in% rownames(seurat_10X2@data) )
# DEgenes_old_up_comp <- dplyr::filter(DEgenes_old_up , gene_id %in% smartseq_data_old$gene_id )
# DEgenes_old_down_comp <- dplyr::filter(DEgenes_old_down , gene_id %in% smartseq_data_old$gene_id )
# DEgenes_old <- c( as.character(DEgenes_old_up$gene_name) , as.character(DEgenes_old_down$gene_name) )
DEgenes_old_ens <- c( as.character(DEgenes_old_up$gene_id) , as.character(DEgenes_old_down$gene_id) )
anno_old <- read.csv(file = "count_table/cell_annotation_NSC_Astro/old/cell_annotation_old.csv")Filter the SMARTseq2 TPM values for the genes that are differentially expressed
q1cells <- filter(anno_old, type == "qNSC1")
q2cells <- filter(anno_old, type == "qNSC2")
astro_cells <- filter(anno_old, type == "Astro")
up_gene_id <- as.character(DEgenes_old_up_comp$gene_id)
down_gene_id <- as.character(DEgenes_old_down_comp$gene_id)
up_gene_names <- as.character(DEgenes_old_up_comp$gene_name)
down_gene_names <- as.character(DEgenes_old_down_comp$gene_name)
scale_red_white <- colorRampPalette(colors = c("red","white"))Fetch the data from qNSC1, qNSC2 and Astrocytes for the genes
df.heatmap.smart_up_comp <- smartseq_data_old %>% dplyr::select( one_of(c("gene_id",as.character(q2cells$cell),as.character(q1cells$cell),as.character(astro_cells$cell))) ) %>% dplyr::filter(gene_id %in% up_gene_id ) %>% arrange(gene_id)
df.heatmap.smart_down_comp <- smartseq_data_old %>% dplyr::select( one_of(c("gene_id",as.character(q2cells$cell),as.character(q1cells$cell),as.character(astro_cells$cell))) ) %>% dplyr::filter(gene_id %in% down_gene_id ) %>% arrange(gene_id)
df.heatmap.smart_comp <- as.matrix( bind_rows( df.heatmap.smart_up_comp , df.heatmap.smart_down_comp ) %>% column_to_rownames("gene_id") )
df.heatmap.smart_comp <- log( as.matrix(df.heatmap.smart_comp) + 1 )
# df.heatmap.smart_comp <- t(scale(x = t(df.heatmap.smart_comp) , center = TRUE , scale = TRUE ))anno_sms2 <- anno_old %>% dplyr::select(cell,type) %>% column_to_rownames("cell")Plot Heatmap
hm_sms2_comp <- pheatmap(mat = pmax( pmin(df.heatmap.smart_comp,3) , -3 ) , cluster_rows = FALSE , cluster_cols = FALSE , color = rev(scale_red_white(200)) , breaks = seq(from=0,to=3,length.out = 200) , main = "log( TPM + 1 )" , show_colnames = FALSE , show_rownames = FALSE , gaps_row = 170 , gaps_col = c(17,113) , fontsize = 8 , annotation_col = anno_sms2 , annotation_colors = list( type = c(qNSC1="steelblue",qNSC2="steelblue1",Astro="grey")) )
SessionInfo
sessionInfo()R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] hash_2.2.6 ggbeeswarm_0.6.0 ggrepel_0.8.0 genefilter_1.60.0
[5] BiocParallel_1.12.0 DESeq2_1.16.1 SummarizedExperiment_1.8.1 DelayedArray_0.4.1
[9] matrixStats_0.53.1 GenomicRanges_1.30.3 GenomeInfoDb_1.14.0 pheatmap_1.0.10
[13] bindrcpp_0.2 org.Mm.eg.db_3.5.0 AnnotationDbi_1.40.0 IRanges_2.12.0
[17] S4Vectors_0.16.0 Seurat_2.2.0 cowplot_0.7.0 clusterProfiler_3.4.4
[21] DOSE_3.4.0 gridExtra_2.2.1 forcats_0.3.0 stringr_1.3.1
[25] dplyr_0.7.4 purrr_0.2.4 readr_1.1.1 tidyr_0.7.2
[29] tibble_1.4.2 ggplot2_2.2.1 tidyverse_1.2.1 Matrix_1.2-14
[33] Biobase_2.38.0 BiocGenerics_0.24.0
loaded via a namespace (and not attached):
[1] R.utils_2.6.0 tidyselect_0.2.3 lme4_1.1-18-1 RSQLite_2.1.1 htmlwidgets_1.2
[6] grid_3.4.3 trimcluster_0.1-2 ranger_0.6.0 Rtsne_0.11 munsell_0.4.3
[11] codetools_0.2-15 ica_1.0-2 colorspace_1.3-2 GOSemSim_2.4.1 knitr_1.20
[16] rstudioapi_0.7 ROCR_1.0-7 robustbase_0.92-7 dtw_1.20-1 NMF_0.20.6
[21] labeling_0.3 lars_1.2 GenomeInfoDbData_1.0.0 mnormt_1.5-5 bit64_0.9-7
[26] rprojroot_1.3-2 diptest_0.75-7 R6_2.2.2 doParallel_1.0.10 VGAM_1.0-3
[31] locfit_1.5-9.1 flexmix_2.3-13 bitops_1.0-6 fgsea_1.4.1 assertthat_0.2.0
[36] SDMTools_1.1-221 scales_0.5.0 nnet_7.3-12 beeswarm_0.2.3 gtable_0.2.0
[41] rlang_0.2.2 MatrixModels_0.4-1 scatterplot3d_0.3-41 splines_3.4.3 lazyeval_0.2.0
[46] ModelMetrics_1.1.0 acepack_1.4.1 broom_0.4.3 checkmate_1.8.5 yaml_2.2.0
[51] reshape2_1.4.2 abind_1.4-5 modelr_0.1.1 backports_1.1.2 qvalue_2.10.0
[56] Hmisc_4.1-1 caret_6.0-73 tools_3.4.3 psych_1.7.8 gridBase_0.4-7
[61] gplots_3.0.1 RColorBrewer_1.1-2 proxy_0.4-22 ggridges_0.5.0 Rcpp_0.12.18
[66] plyr_1.8.4 base64enc_0.1-3 zlibbioc_1.24.0 RCurl_1.95-4.10 rpart_4.1-12
[71] pbapply_1.3-1 haven_1.1.2 cluster_2.0.6 magrittr_1.5 data.table_1.11.6
[76] DO.db_2.9 openxlsx_4.1.0 mvtnorm_1.0-5 evaluate_0.11 hms_0.4.2
[81] xtable_1.8-2 XML_3.98-1.11 rio_0.5.10 mclust_5.2.2 readxl_1.1.0
[86] compiler_3.4.3 KernSmooth_2.23-15 crayon_1.3.4 minqa_1.2.4 R.oo_1.22.0
[91] htmltools_0.3.6 segmented_0.5-1.4 Formula_1.2-3 geneplotter_1.56.0 tclust_1.2-3
[96] lubridate_1.7.1 DBI_1.0.0 diffusionMap_1.1-0.1 MASS_7.3-48 fpc_2.1-10
[101] boot_1.3-20 car_3.0-2 cli_1.0.0 R.methodsS3_1.7.1 gdata_2.17.0
[106] bindr_0.1 igraph_1.0.1 pkgconfig_2.0.2 sn_1.5-0 rvcheck_0.1.0
[111] registry_0.3 numDeriv_2016.8-1 foreign_0.8-69 xml2_1.1.1 foreach_1.4.3
[116] annotate_1.56.2 vipor_0.4.5 rngtools_1.2.4 pkgmaker_0.22 XVector_0.18.0
[121] rvest_0.3.2 digest_0.6.12 tsne_0.1-3 rmarkdown_1.10 cellranger_1.1.0
[126] fastmatch_1.1-0 htmlTable_1.12 curl_3.2 kernlab_0.9-25 gtools_3.5.0
[131] modeltools_0.2-22 nloptr_1.0.4 nlme_3.1-131 jsonlite_1.5 carData_3.0-1
[136] viridisLite_0.3.0 pillar_1.3.0 lattice_0.20-35 httr_1.3.1 DEoptimR_1.0-8
[141] survival_2.41-3 GO.db_3.5.0 glue_1.3.0 zip_1.0.0 FNN_1.1
[146] prabclus_2.2-6 iterators_1.0.8 bit_1.1-14 class_7.3-14 stringi_1.2.4
[151] mixtools_1.0.4 blob_1.1.1 latticeExtra_0.6-28 caTools_1.17.1 memoise_1.0.0
[156] irlba_2.1.2 ape_5.1 ---
title: "Analysis of old and young neural stem cells sequenced with the SmartSeq2 protocol"
output: 
  html_notebook: 
    smart: no
    df_print: paged
---

# Load data for young and old neural stem cells sequenced with the Smart-Seq2 protocol

```{r}
NSCs_annotation <- read.csv(file = "count_table/NSCs_annotation.csv" , row.names = 1)

TPM_NSCs <- read.csv(file = "count_table/TPM_NSCs.csv" , row.names = 1)
```

Make the cell names also rownames

```{r}
rownames(NSCs_annotation) <- NSCs_annotation$cell

head( NSCs_annotation )
```

# Load the R packages needed for the analysis

We need the Matrix package to use sparse matrices to save memory while computing. The ggplot2 package is used for plotting. Seurat is used to create PCA and tSNE plots. 

```{r}
require(Matrix)
require(tidyverse)
require(ggplot2)
require(gridExtra)

require(clusterProfiler)
require(Seurat)
```

```{r}
set.seed(123)
```

Load the monocle pseudotime results

```{r}
pseudotime_ordering <- read.csv(file = "Monocle/pseudotime_ordering.csv" , row.names = 1 )
```


# Check innate immune response and interferon response GO category genes

## Create a Seurat object using the TPM values for the SmartSeq2 data and use the same normalization strategy as for the 10X sequencing run.

Use the TPM data.frame to initialize a Seurat object

```{r}
TPM_NSC <- remove_rownames(TPM_NSCs) %>% column_to_rownames( var = "ensembl_gene_id")

seurat_sms2 <- CreateSeuratObject(raw.data = TPM_NSC  , min.cells = 3, project = "young_vs_old_SmartSeq2")
```

Set identity to NSC to begin with.

```{r}
seurat_sms2 <- SetIdent(object = seurat_sms2 , ident.use = "NSC")
```


```{r}
VlnPlot(object = seurat_sms2, features.plot = c("nGene", "nUMI"))
```

Add metadata: age of the animal, cell name and celltype (activation state)

```{r}
seurat_sms2 <- AddMetaData(object = seurat_sms2, metadata = NSCs_annotation )
```

Log-normalize the TPM data

```{r}
seurat_sms2 <- NormalizeData(object = seurat_sms2, normalization.method = "LogNormalize")
```


```{r results='hide'}
seurat_sms2 <- ScaleData(object = seurat_sms2, vars.to.regress = c("nUMI", "nGene"))
```

### Regress out difference between S phase and G2/M Phase

First we load the genes related to the cell cycle phases G2/M and S and make them matching to the gene symbols in the dataset by converting to ENSEMBL_IDs . 

```{r}
s.genes <- bitr(geneID = stringr::str_to_title(cc.genes$s.genes) , fromType = "SYMBOL" , toType = "ENSEMBL" ,drop = FALSE ,OrgDb = "org.Mm.eg.db")

g2m.genes <- bitr(geneID = stringr::str_to_title(cc.genes$g2m.genes) , fromType = "SYMBOL" , toType = "ENSEMBL" ,drop = FALSE ,OrgDb = "org.Mm.eg.db")

seurat_sms2 <- CellCycleScoring(object = seurat_sms2, s.genes = s.genes$ENSEMBL , g2m.genes = g2m.genes$ENSEMBL , set.ident = FALSE)
```

Then we calculate the difference between the cell cycle scores and regress our data on that difference.

```{r}
seurat_sms2@meta.data$CC.Difference <- seurat_sms2@meta.data$S.Score - seurat_sms2@meta.data$G2M.Score

seurat_sms2 <- ScaleData(object = seurat_sms2, vars.to.regress = "CC.Difference", display.progress = FALSE)
```

## Load a list of genes from the innate immune response GO category 

```{r}
results_interferon_response_immune <- structure(list(mgi_symbol = c("Uba7", "Ube2l6", "Ube2e1", "Ube2e2", 
"Isg15", "Ikbke", "Setd2", "A230050P20Rik", "Mx2", "Shmt2", "Irgm2", 
"Dnaja3", "Kynu", "Bst2", "Sec61a1", "Cited1", "H2-Aa", "Slc11a1", 
"Gch1", "Ubd", "Il23r", "Slc30a8", "Cxcl16", "Cyp27b1", "Trim21", 
"Ifitm2", "Ifitm1", "Ifitm3", "Adamts13", "Tgtp2", "Tgtp1", "Snca", 
"Cd40", "Mefv", "Ciita", "Klhl20", "Adar", "Ifnar2", "Eif2ak2", 
"Plscr1", "Lamp3", "Xaf1", "Stat1", "S100a8", "Trdc", "S100a9", 
"Aim2", "Mb21d1", "Irf1", "Pglyrp4", "Sdhaf4", "Ddx3x", "Defb1", 
"Serinc5", "Ddx58", "Defb12", "Defb34", "Nlrp10", "Rela", "Gata3", 
"Csf1r", "Igha", "Defb15", "Rnase6", "Defb35", "Defb13", "Pglyrp3", 
"Ighg2c", "Krt16", "Tril", "Nlrp9c", "Bpifb3", "Pik3ca", "Nlrp1a", 
"Nlrp9b", "Ighg3", "Ighd", "Ighm", "Nlrp1b", "Cfi", "Ly96", "Jchain", 
"C8b", "Nlrx1", "Cybb", "Pml", "C8a", "Tnk1", "Polr3c", "Il1rap", 
"Rbm14", "Polr3e", "Anxa1", "Cfd", "Ifnk", "Fcnb", "Nr1h4", "Defb43", 
"Nono", "5730559C18Rik", "Hmgb3", "Defb30", "Sh2d1b2", "Ighv5-2", 
"Ighv2-2", "Tnfaip8l2", "Tarm1", "Ighv5-4", "Bpifa1", "Nod2", 
"Ighv2-3", "Otulin", "Ighv5-6", "Bpifb1", "Ifi202b", "Ighv5-9", 
"Fcgr1", "Trim28", "Arg1", "Trbc1", "Pik3cg", "Defb18", "Defb41", 
"Ighv2-5", "Ighv5-12", "Ighv2-6", "Ighv5-9-1", "Polr3g", "Cyld", 
"Sh2d1a", "Zap70", "Ifit3", "Rsad2", "Msrb1", "Ifit1", "Cr1l", 
"Cfp", "Sp110", "Trbc2", "Casp4", "Nod1", "Sla2", "Smpdl3b", 
"Tlr5", "Ighv5-12-4", "Ighv2-9-1", "Axl", "Map3k5", "Cd55", "Blk", 
"Myd88", "Tnfsf4", "Ighv2-6-8", "Ighv2-7", "Ighv5-15", "Ighv5-16", 
"Ighv5-17", "Ighv2-9", "Irf5", "Ighv7-1", "Ighv7-2", "Ighv14-1", 
"Ighv4-1", "Ighv3-1", "Ighv11-1", "Trim11", "Sla", "Ighv14-2", 
"Ighv11-2", "Ighv14-3", "Trim14", "Ighv16-1", "Ighv9-1", "Dhx58", 
"Trim13", "Pqbp1", "Cyba", "Ighv9-2", "Ighv9-3", "Ighv7-3", "Ankhd1", 
"Ighv14-4", "Ighv3-3", "Oasl2", "Ighv7-4", "Ighv3-4", "Nrros", 
"Tlr13", "Fes", "Ticam1", "Ighv3-6", "Ighv9-4", "Apobec3", "Ighv3-8", 
"Ighv13-2", "Ankrd17", "Trim15", "Ighv12-3", "Ighv6-3", "Oasl1", 
"Trim10", "Tlr1", "Ighv6-4", "C4bp", "Trim5", "Tlr6", "Trim31", 
"Polr3b", "Mavs", "Vnn1", "Apcs", "Akirin2", "Ssc5d", "Trim12a", 
"Polr3d", "Clec5a", "Fgg", "Dab2ip", "Hmgb2", "Fga", "Fgb", "Trim12c", 
"Fgr", "Ipo7", "Mst1r", "Trim30b", "Trim30c", "Sfpq", "Il34", 
"Trim25", "Trim30a", "Trim30d", "Pspc1", "Card9", "Mbl1", "Ptk2", 
"Lcn2", "Abl1", "Sftpd", "Tlr8", "Pycard", "Ighv6-5", "Tlr2", 
"Ighv6-6", "Ighv6-7", "Serping1", "Tlr7", "Src", "Ighv8-2", "Iglc1", 
"Ighv10-1", "Ighv1-4", "Il1f6", "Iglc4", "Ighv1-5", "Ighv10-3", 
"Ighv1-7", "Iglc2", "Defb23", "Gper1", "Cnpy3", "Trdv4", "Defb20", 
"Defb22", "Defb26", "Defb28", "Il1f5", "Defb29", "Ighv15-2", 
"Ighv1-9", "Defb21", "Itch", "Pglyrp1", "Nfkb1", "Defb19", "Defb45", 
"Il1rl2", "Defb36", "Mid2", "Defb25", "Ighv1-11", "Ighv1-12", 
"Ighv1-15", "Ighv1-16", "Grb2", "Ighv1-18", "Ighv1-19", "Ighv1-20", 
"Map4k2", "Il1f8", "Pik3cb", "Adam15", "Ighv1-22", "Bcl10", "Ighv1-23", 
"Ighv1-24", "Ighv1-26", "Prkdc", "Irak4", "F2rl1", "Adgrb1", 
"Isg20", "C1qb", "C1qc", "C1qa", "Mr1", "Polr3a", "Tlr9", "Ighv1-76", 
"Optn", "Ighv1-77", "Ptx3", "Ighv1-78", "Zbtb1", "Grap", "Lbp", 
"Arhgef2", "Mcoln2", "Ighv1-80", "Tbk1", "Ighv1-81", "Ifih1", 
"Ighv1-31", "Bmx", "Ighv1-82", "Ighv1-34", "Ly86", "Nlrc5", "Ighv1-85", 
"Polr3f", "Ighv1-36", "Ighv1-37", "Tlr11", "Trim27", "Malt1", 
"Fadd", "Ighv1-39", "Ighv1-42", "Ighv1-43", "Btk", "Dcst1", "Parp14", 
"Ighv1-47", "Ighv8-4", "Ighv1-49", "Itk", "Ighv8-5", "Ighv1-50", 
"Trim26", "Chid1", "Csk", "Fcer1g", "Ighv1-52", "Ighv1-53", "Ighv8-6", 
"Naip2", "Nlrp3", "C8g", "Ighv1-54", "Csf1", "Traf3", "Ighv1-55", 
"Ighv1-56", "Elf4", "Dtx3l", "Irak1", "Naip6", "Parp9", "Ighv8-8", 
"Ighv1-58", "Pik3cd", "Hck", "Ighv1-59", "Cd180", "Irf3", "Ighv1-61", 
"Ighv1-62-1", "Ifnl2", "Relb", "Ifnl3", "Fyn", "Ighv1-62-2", 
"Ighv1-62-3", "Hmgb1", "Tlr12", "Ighv8-9", "Yes1", "Ighv1-63", 
"Ighv1-64", "Tbkbp1", "Siglecg", "Ighv8-11", "Ighv1-66", "Cfh", 
"Zc3hav1", "Ighv1-67", "Ighv1-69", "Ighv8-12", "Havcr2", "4933415F23Rik", 
"Trim32", "Ighv8-13", "C3", "Ighv1-74", "Trim62", "Hexim1", "Matr3", 
"Ighv1-75", "Slpi", "Zfp809", "Igll1", "Arid5a", "Tollip", "Tlr4", 
"Syk", "C4b", "Cd244", "Fbxo9", "Ly9", "Ifnb1", "Zfp683", "Ifna15", 
"Ifna9", "Ifna14", "Ifna12", "Lck", "Serinc3", "Nlrp6", "Camp", 
"Ifna13", "Ifna16", "Ifnab", "Lyn", "Gm13271", "Gm13283", "Gm13290", 
"Gm13289", "Gm13272", "Cd24a", "Spon2", "Lgr4", "Ifnz", "Gm13276", 
"Gm13277", "Gm13278", "Tlr3", "Gm13275", "Gm13279", "Gm13285", 
"Gm13287", "Gm13288", "Ifna11", "Ifna4", "Oas2", "Prkd1", "Oas3", 
"Slamf1", "Marco", "Ifne", "Rarres2", "Clec4a2", "Treml4", "Oas1a", 
"Slamf6", "Fcna", "Sarm1", "Atg5", "Trim59", "Clec4n", "Herc6", 
"Ripk2", "Irgm1", "Prdm1", "Hc", "Clec4d", "Clec4e", "Ticam2", 
"Trem2", "Il27", "Frk", "Mif", "Ptk2b", "C1rl", "Irf7", "C1ra", 
"C1rb", "C1s2", "Polr3k", "Zbp1", "Trim35", "Il23a", "C2", "Cfb", 
"Padi4", "Cactin", "Masp2", "Klrg1", "Arg2", "Ptk6", "Clec7a", 
"Rnf135", "Txk", "Klrk1", "Sec14l1", "Crcp", "Styk1", "Tmem173", 
"Atg12", "Gm17416", "Iigp1", "Polr3h", "Riok3", "Cd14", "Gm5849", 
"Mbl2", "Akap8", "Xrcc5", "Nfkb2", "Masp1", "Trim8", "Pcbp2", 
"Ppp1r14b", "Gsdmd", "Fer", "Grap2", "Nlrc4", "Map4k2"), external_gene_name = c("Uba7", 
"Ube2l6", "Ube2e1", "Ube2e2", "Isg15", "Ikbke", "Setd2", "A230050P20Rik", 
"Mx2", "Shmt2", "Irgm2", "Dnaja3", "Kynu", "Bst2", "Sec61a1", 
"Cited1", "H2-Aa", "Slc11a1", "Gch1", "Ubd", "Il23r", "Slc30a8", 
"Cxcl16", "Cyp27b1", "Trim21", "Ifitm2", "Ifitm1", "Ifitm3", 
"Adamts13", "Tgtp2", "Tgtp1", "Snca", "Cd40", "Mefv", "Ciita", 
"Klhl20", "Adar", "Ifnar2", "Eif2ak2", "Plscr1", "Lamp3", "Xaf1", 
"Stat1", "S100a8", "Trdc", "S100a9", "Aim2", "Mb21d1", "Irf1", 
"Pglyrp4", "Sdhaf4", "Ddx3x", "Defb1", "Serinc5", "Ddx58", "Defb12", 
"Defb34", "Nlrp10", "Rela", "Gata3", "Csf1r", "Igha", "Defb15", 
"Rnase6", "Defb35", "Defb13", "Pglyrp3", "Ighg2c", "Krt16", "Tril", 
"Nlrp9c", "Bpifb3", "Pik3ca", "Nlrp1a", "Nlrp9b", "Ighg3", "Ighd", 
"Ighm", "Nlrp1b", "Cfi", "Ly96", "Jchain", "C8b", "Nlrx1", "Cybb", 
"Pml", "C8a", "Tnk1", "Polr3c", "Il1rap", "Rbm14", "Polr3e", 
"Anxa1", "Cfd", "Ifnk", "Fcnb", "Nr1h4", "Defb43", "Nono", "5730559C18Rik", 
"Hmgb3", "Defb30", "Sh2d1b2", "Ighv5-2", "Ighv2-2", "Tnfaip8l2", 
"Tarm1", "Ighv5-4", "Bpifa1", "Nod2", "Ighv2-3", "Otulin", "Ighv5-6", 
"Bpifb1", "Ifi202b", "Ighv5-9", "Fcgr1", "Trim28", "Arg1", "Trbc1", 
"Pik3cg", "Defb18", "Defb41", "Ighv2-5", "Ighv5-12", "Ighv2-6", 
"Ighv5-9-1", "Polr3g", "Cyld", "Sh2d1a", "Zap70", "Ifit3", "Rsad2", 
"Msrb1", "Ifit1", "Cr1l", "Cfp", "Sp110", "Trbc2", "Casp4", "Nod1", 
"Sla2", "Smpdl3b", "Tlr5", "Ighv5-12-4", "Ighv2-9-1", "Axl", 
"Map3k5", "Cd55", "Blk", "Myd88", "Tnfsf4", "Ighv2-6-8", "Ighv2-7", 
"Ighv5-15", "Ighv5-16", "Ighv5-17", "Ighv2-9", "Irf5", "Ighv7-1", 
"Ighv7-2", "Ighv14-1", "Ighv4-1", "Ighv3-1", "Ighv11-1", "Trim11", 
"Sla", "Ighv14-2", "Ighv11-2", "Ighv14-3", "Trim14", "Ighv16-1", 
"Ighv9-1", "Dhx58", "Trim13", "Pqbp1", "Cyba", "Ighv9-2", "Ighv9-3", 
"Ighv7-3", "Ankhd1", "Ighv14-4", "Ighv3-3", "Oasl2", "Ighv7-4", 
"Ighv3-4", "Nrros", "Tlr13", "Fes", "Ticam1", "Ighv3-6", "Ighv9-4", 
"Apobec3", "Ighv3-8", "Ighv13-2", "Ankrd17", "Trim15", "Ighv12-3", 
"Ighv6-3", "Oasl1", "Trim10", "Tlr1", "Ighv6-4", "C4bp", "Trim5", 
"Tlr6", "Trim31", "Polr3b", "Mavs", "Vnn1", "Apcs", "Akirin2", 
"Ssc5d", "Trim12a", "Polr3d", "Clec5a", "Fgg", "Dab2ip", "Hmgb2", 
"Fga", "Fgb", "Trim12c", "Fgr", "Ipo7", "Mst1r", "Trim30b", "Trim30c", 
"Sfpq", "Il34", "Trim25", "Trim30a", "Trim30d", "Pspc1", "Card9", 
"Mbl1", "Ptk2", "Lcn2", "Abl1", "Sftpd", "Tlr8", "Pycard", "Ighv6-5", 
"Tlr2", "Ighv6-6", "Ighv6-7", "Serping1", "Tlr7", "Src", "Ighv8-2", 
"Iglc1", "Ighv10-1", "Ighv1-4", "Il1f6", "Iglc4", "Ighv1-5", 
"Ighv10-3", "Ighv1-7", "Iglc2", "Defb23", "Gper1", "Cnpy3", "Trdv4", 
"Defb20", "Defb22", "Defb26", "Defb28", "Il1f5", "Defb29", "Ighv15-2", 
"Ighv1-9", "Defb21", "Itch", "Pglyrp1", "Nfkb1", "Defb19", "Defb45", 
"Il1rl2", "Defb36", "Mid2", "Defb25", "Ighv1-11", "Ighv1-12", 
"Ighv1-15", "Ighv1-16", "Grb2", "Ighv1-18", "Ighv1-19", "Ighv1-20", 
"Map4k2", "Il1f8", "Pik3cb", "Adam15", "Ighv1-22", "Bcl10", "Ighv1-23", 
"Ighv1-24", "Ighv1-26", "Prkdc", "Irak4", "F2rl1", "Adgrb1", 
"Isg20", "C1qb", "C1qc", "C1qa", "Mr1", "Polr3a", "Tlr9", "Ighv1-76", 
"Optn", "Ighv1-77", "Ptx3", "Ighv1-78", "Zbtb1", "Grap", "Lbp", 
"Arhgef2", "Mcoln2", "Ighv1-80", "Tbk1", "Ighv1-81", "Ifih1", 
"Ighv1-31", "Bmx", "Ighv1-82", "Ighv1-34", "Ly86", "Nlrc5", "Ighv1-85", 
"Polr3f", "Ighv1-36", "Ighv1-37", "Tlr11", "Trim27", "Malt1", 
"Fadd", "Ighv1-39", "Ighv1-42", "Ighv1-43", "Btk", "Dcst1", "Parp14", 
"Ighv1-47", "Ighv8-4", "Ighv1-49", "Itk", "Ighv8-5", "Ighv1-50", 
"Trim26", "Chid1", "Csk", "Fcer1g", "Ighv1-52", "Ighv1-53", "Ighv8-6", 
"Naip2", "Nlrp3", "C8g", "Ighv1-54", "Csf1", "Traf3", "Ighv1-55", 
"Ighv1-56", "Elf4", "Dtx3l", "Irak1", "Naip6", "Parp9", "Ighv8-8", 
"Ighv1-58", "Pik3cd", "Hck", "Ighv1-59", "Cd180", "Irf3", "Ighv1-61", 
"Ighv1-62-1", "Ifnl2", "Relb", "Ifnl3", "Fyn", "Ighv1-62-2", 
"Ighv1-62-3", "Hmgb1", "Tlr12", "Ighv8-9", "Yes1", "Ighv1-63", 
"Ighv1-64", "Tbkbp1", "Siglecg", "Ighv8-11", "Ighv1-66", "Cfh", 
"Zc3hav1", "Ighv1-67", "Ighv1-69", "Ighv8-12", "Havcr2", "4933415F23Rik", 
"Trim32", "Ighv8-13", "C3", "Ighv1-74", "Trim62", "Hexim1", "Matr3", 
"Ighv1-75", "Slpi", "Zfp809", "Igll1", "Arid5a", "Tollip", "Tlr4", 
"Syk", "C4b", "Cd244", "Fbxo9", "Ly9", "Ifnb1", "Zfp683", "Ifna15", 
"Ifna9", "Ifna14", "Ifna12", "Lck", "Serinc3", "Nlrp6", "Camp", 
"Ifna13", "Ifna16", "Ifnab", "Lyn", "Gm13271", "Gm13283", "Gm13290", 
"Gm13289", "Gm13272", "Cd24a", "Spon2", "Lgr4", "Ifnz", "Gm13276", 
"Gm13277", "Gm13278", "Tlr3", "Gm13275", "Gm13279", "Gm13285", 
"Gm13287", "Gm13288", "Ifna11", "Ifna4", "Oas2", "Prkd1", "Oas3", 
"Slamf1", "Marco", "Ifne", "Rarres2", "Clec4a2", "Treml4", "Oas1a", 
"Slamf6", "Fcna", "Sarm1", "Atg5", "Trim59", "Clec4n", "Herc6", 
"Ripk2", "Irgm1", "Prdm1", "Hc", "Clec4d", "Clec4e", "Ticam2", 
"Trem2", "Il27", "Frk", "Mif", "Ptk2b", "C1rl", "Irf7", "C1ra", 
"C1rb", "C1s2", "Polr3k", "Zbp1", "Trim35", "Il23a", "C2", "Cfb", 
"Padi4", "Cactin", "Masp2", "Klrg1", "Arg2", "Ptk6", "Clec7a", 
"Rnf135", "Txk", "Klrk1", "Sec14l1", "Crcp", "Styk1", "Tmem173", 
"Atg12", "Gm17416", "Iigp1", "Polr3h", "Riok3", "Cd14", "Gm5849", 
"Mbl2", "Akap8", "Xrcc5", "Nfkb2", "Masp1", "Trim8", "Pcbp2", 
"Ppp1r14b", "Gsdmd", "Fer", "Grap2", "Nlrc4", "Map4k2"), ensembl_gene_id = c("ENSMUSG00000032596", 
"ENSMUSG00000027078", "ENSMUSG00000021774", "ENSMUSG00000058317", 
"ENSMUSG00000035692", "ENSMUSG00000042349", "ENSMUSG00000044791", 
"ENSMUSG00000038884", "ENSMUSG00000023341", "ENSMUSG00000025403", 
"ENSMUSG00000069874", "ENSMUSG00000004069", "ENSMUSG00000026866", 
"ENSMUSG00000046718", "ENSMUSG00000030082", "ENSMUSG00000051159", 
"ENSMUSG00000036594", "ENSMUSG00000026177", "ENSMUSG00000037580", 
"ENSMUSG00000035186", "ENSMUSG00000049093", "ENSMUSG00000022315", 
"ENSMUSG00000018920", "ENSMUSG00000006724", "ENSMUSG00000030966", 
"ENSMUSG00000060591", "ENSMUSG00000025491", "ENSMUSG00000025492", 
"ENSMUSG00000014852", "ENSMUSG00000078921", "ENSMUSG00000078922", 
"ENSMUSG00000025889", "ENSMUSG00000017652", "ENSMUSG00000022534", 
"ENSMUSG00000022504", "ENSMUSG00000026705", "ENSMUSG00000027951", 
"ENSMUSG00000022971", "ENSMUSG00000024079", "ENSMUSG00000032369", 
"ENSMUSG00000041247", "ENSMUSG00000040483", "ENSMUSG00000026104", 
"ENSMUSG00000056054", "ENSMUSG00000104876", "ENSMUSG00000056071", 
"ENSMUSG00000037860", "ENSMUSG00000032344", "ENSMUSG00000018899", 
"ENSMUSG00000042250", "ENSMUSG00000026154", "ENSMUSG00000000787", 
"ENSMUSG00000044748", "ENSMUSG00000021703", "ENSMUSG00000040296", 
"ENSMUSG00000043787", "ENSMUSG00000052554", "ENSMUSG00000049709", 
"ENSMUSG00000024927", "ENSMUSG00000015619", "ENSMUSG00000024621", 
"ENSMUSG00000095079", "ENSMUSG00000048500", "ENSMUSG00000021880", 
"ENSMUSG00000058052", "ENSMUSG00000044222", "ENSMUSG00000042244", 
"ENSMUSG00000076612", "ENSMUSG00000053797", "ENSMUSG00000043496", 
"ENSMUSG00000040614", "ENSMUSG00000068008", "ENSMUSG00000027665", 
"ENSMUSG00000069830", "ENSMUSG00000060508", "ENSMUSG00000076615", 
"ENSMUSG00000104213", "ENSMUSG00000076617", "ENSMUSG00000070390", 
"ENSMUSG00000058952", "ENSMUSG00000025779", "ENSMUSG00000067149", 
"ENSMUSG00000029656", "ENSMUSG00000032109", "ENSMUSG00000015340", 
"ENSMUSG00000036986", "ENSMUSG00000035031", "ENSMUSG00000001583", 
"ENSMUSG00000028099", "ENSMUSG00000022514", "ENSMUSG00000006456", 
"ENSMUSG00000030880", "ENSMUSG00000024659", "ENSMUSG00000061780", 
"ENSMUSG00000042993", "ENSMUSG00000026835", "ENSMUSG00000047638", 
"ENSMUSG00000075572", "ENSMUSG00000031311", "ENSMUSG00000041605", 
"ENSMUSG00000015217", "ENSMUSG00000075571", "ENSMUSG00000073494", 
"ENSMUSG00000076633", "ENSMUSG00000096464", "ENSMUSG00000013707", 
"ENSMUSG00000053338", "ENSMUSG00000095612", "ENSMUSG00000027483", 
"ENSMUSG00000055994", "ENSMUSG00000094164", "ENSMUSG00000046034", 
"ENSMUSG00000094951", "ENSMUSG00000027485", "ENSMUSG00000026535", 
"ENSMUSG00000095285", "ENSMUSG00000015947", "ENSMUSG00000005566", 
"ENSMUSG00000019987", "ENSMUSG00000076490", "ENSMUSG00000020573", 
"ENSMUSG00000073735", "ENSMUSG00000067773", "ENSMUSG00000096498", 
"ENSMUSG00000095429", "ENSMUSG00000096670", "ENSMUSG00000095210", 
"ENSMUSG00000035834", "ENSMUSG00000036712", "ENSMUSG00000005696", 
"ENSMUSG00000026117", "ENSMUSG00000074896", "ENSMUSG00000020641", 
"ENSMUSG00000075705", "ENSMUSG00000034459", "ENSMUSG00000016481", 
"ENSMUSG00000001128", "ENSMUSG00000070034", "ENSMUSG00000076498", 
"ENSMUSG00000033538", "ENSMUSG00000038058", "ENSMUSG00000027636", 
"ENSMUSG00000028885", "ENSMUSG00000079164", "ENSMUSG00000103033", 
"ENSMUSG00000095565", "ENSMUSG00000002602", "ENSMUSG00000071369", 
"ENSMUSG00000026399", "ENSMUSG00000014453", "ENSMUSG00000032508", 
"ENSMUSG00000026700", "ENSMUSG00000076646", "ENSMUSG00000096824", 
"ENSMUSG00000094134", "ENSMUSG00000094194", "ENSMUSG00000095571", 
"ENSMUSG00000096638", "ENSMUSG00000029771", "ENSMUSG00000076665", 
"ENSMUSG00000076653", "ENSMUSG00000094509", "ENSMUSG00000076655", 
"ENSMUSG00000093838", "ENSMUSG00000094533", "ENSMUSG00000020455", 
"ENSMUSG00000022372", "ENSMUSG00000095583", "ENSMUSG00000096108", 
"ENSMUSG00000095642", "ENSMUSG00000039853", "ENSMUSG00000076661", 
"ENSMUSG00000096805", "ENSMUSG00000017830", "ENSMUSG00000035235", 
"ENSMUSG00000031157", "ENSMUSG00000006519", "ENSMUSG00000094102", 
"ENSMUSG00000096459", "ENSMUSG00000076652", "ENSMUSG00000024483", 
"ENSMUSG00000076666", "ENSMUSG00000094029", "ENSMUSG00000029561", 
"ENSMUSG00000076668", "ENSMUSG00000103939", "ENSMUSG00000052384", 
"ENSMUSG00000033777", "ENSMUSG00000053158", "ENSMUSG00000047123", 
"ENSMUSG00000076672", "ENSMUSG00000094322", "ENSMUSG00000009585", 
"ENSMUSG00000076674", "ENSMUSG00000076671", "ENSMUSG00000055204", 
"ENSMUSG00000050747", "ENSMUSG00000076676", "ENSMUSG00000076677", 
"ENSMUSG00000041827", "ENSMUSG00000073400", "ENSMUSG00000044827", 
"ENSMUSG00000094174", "ENSMUSG00000026405", "ENSMUSG00000060441", 
"ENSMUSG00000051498", "ENSMUSG00000058063", "ENSMUSG00000034453", 
"ENSMUSG00000037523", "ENSMUSG00000037440", "ENSMUSG00000026542", 
"ENSMUSG00000028291", "ENSMUSG00000035279", "ENSMUSG00000066258", 
"ENSMUSG00000000776", "ENSMUSG00000029915", "ENSMUSG00000033860", 
"ENSMUSG00000026883", "ENSMUSG00000054717", "ENSMUSG00000028001", 
"ENSMUSG00000033831", "ENSMUSG00000057143", "ENSMUSG00000028874", 
"ENSMUSG00000066232", "ENSMUSG00000032584", "ENSMUSG00000052749", 
"ENSMUSG00000078616", "ENSMUSG00000028820", "ENSMUSG00000031750", 
"ENSMUSG00000000275", "ENSMUSG00000030921", "ENSMUSG00000057596", 
"ENSMUSG00000021938", "ENSMUSG00000026928", "ENSMUSG00000037780", 
"ENSMUSG00000022607", "ENSMUSG00000026822", "ENSMUSG00000026842", 
"ENSMUSG00000021795", "ENSMUSG00000040522", "ENSMUSG00000030793", 
"ENSMUSG00000096407", "ENSMUSG00000027995", "ENSMUSG00000076680", 
"ENSMUSG00000087582", "ENSMUSG00000023224", "ENSMUSG00000044583", 
"ENSMUSG00000027646", "ENSMUSG00000102301", "ENSMUSG00000105906", 
"ENSMUSG00000095981", "ENSMUSG00000095442", "ENSMUSG00000026984", 
"ENSMUSG00000106039", "ENSMUSG00000096499", "ENSMUSG00000095700", 
"ENSMUSG00000095200", "ENSMUSG00000076937", "ENSMUSG00000074681", 
"ENSMUSG00000053647", "ENSMUSG00000023973", "ENSMUSG00000076867", 
"ENSMUSG00000049560", "ENSMUSG00000027468", "ENSMUSG00000074680", 
"ENSMUSG00000074679", "ENSMUSG00000026983", "ENSMUSG00000044249", 
"ENSMUSG00000076688", "ENSMUSG00000094694", "ENSMUSG00000056544", 
"ENSMUSG00000027598", "ENSMUSG00000030413", "ENSMUSG00000028163", 
"ENSMUSG00000050645", "ENSMUSG00000062124", "ENSMUSG00000070942", 
"ENSMUSG00000044863", "ENSMUSG00000000266", "ENSMUSG00000074678", 
"ENSMUSG00000102888", "ENSMUSG00000095416", "ENSMUSG00000103254", 
"ENSMUSG00000095554", "ENSMUSG00000059923", "ENSMUSG00000076695", 
"ENSMUSG00000096410", "ENSMUSG00000095761", "ENSMUSG00000024948", 
"ENSMUSG00000026985", "ENSMUSG00000032462", "ENSMUSG00000028041", 
"ENSMUSG00000094561", "ENSMUSG00000028191", "ENSMUSG00000103290", 
"ENSMUSG00000094241", "ENSMUSG00000094546", "ENSMUSG00000022672", 
"ENSMUSG00000059883", "ENSMUSG00000021678", "ENSMUSG00000034730", 
"ENSMUSG00000039236", "ENSMUSG00000036905", "ENSMUSG00000036896", 
"ENSMUSG00000036887", "ENSMUSG00000026471", "ENSMUSG00000025280", 
"ENSMUSG00000045322", "ENSMUSG00000093896", "ENSMUSG00000026672", 
"ENSMUSG00000096452", "ENSMUSG00000027832", "ENSMUSG00000096326", 
"ENSMUSG00000033454", "ENSMUSG00000004837", "ENSMUSG00000016024", 
"ENSMUSG00000028059", "ENSMUSG00000011008", "ENSMUSG00000094075", 
"ENSMUSG00000020115", "ENSMUSG00000094689", "ENSMUSG00000026896", 
"ENSMUSG00000096649", "ENSMUSG00000031377", "ENSMUSG00000095127", 
"ENSMUSG00000093955", "ENSMUSG00000021423", "ENSMUSG00000074151", 
"ENSMUSG00000096150", "ENSMUSG00000027427", "ENSMUSG00000094051", 
"ENSMUSG00000095923", "ENSMUSG00000051969", "ENSMUSG00000021326", 
"ENSMUSG00000032688", "ENSMUSG00000031077", "ENSMUSG00000095130", 
"ENSMUSG00000094652", "ENSMUSG00000095859", "ENSMUSG00000031264", 
"ENSMUSG00000042672", "ENSMUSG00000034422", "ENSMUSG00000076709", 
"ENSMUSG00000096355", "ENSMUSG00000076710", "ENSMUSG00000020395", 
"ENSMUSG00000102364", "ENSMUSG00000094198", "ENSMUSG00000024457", 
"ENSMUSG00000025512", "ENSMUSG00000032312", "ENSMUSG00000058715", 
"ENSMUSG00000095204", "ENSMUSG00000093894", "ENSMUSG00000094505", 
"ENSMUSG00000078945", "ENSMUSG00000032691", "ENSMUSG00000015083", 
"ENSMUSG00000094787", "ENSMUSG00000014599", "ENSMUSG00000021277", 
"ENSMUSG00000095589", "ENSMUSG00000094862", "ENSMUSG00000031103", 
"ENSMUSG00000049502", "ENSMUSG00000031392", "ENSMUSG00000078942", 
"ENSMUSG00000022906", "ENSMUSG00000104452", "ENSMUSG00000095889", 
"ENSMUSG00000039936", "ENSMUSG00000003283", "ENSMUSG00000095197", 
"ENSMUSG00000021624", "ENSMUSG00000003184", "ENSMUSG00000094087", 
"ENSMUSG00000102313", "ENSMUSG00000059128", "ENSMUSG00000002983", 
"ENSMUSG00000060747", "ENSMUSG00000019843", "ENSMUSG00000096078", 
"ENSMUSG00000096767", "ENSMUSG00000066551", "ENSMUSG00000062545", 
"ENSMUSG00000095117", "ENSMUSG00000014932", "ENSMUSG00000096672", 
"ENSMUSG00000094088", "ENSMUSG00000038517", "ENSMUSG00000030468", 
"ENSMUSG00000095170", "ENSMUSG00000095519", "ENSMUSG00000026365", 
"ENSMUSG00000029826", "ENSMUSG00000095863", "ENSMUSG00000094502", 
"ENSMUSG00000076731", "ENSMUSG00000020399", "ENSMUSG00000073730", 
"ENSMUSG00000051675", "ENSMUSG00000076733", "ENSMUSG00000024164", 
"ENSMUSG00000094124", "ENSMUSG00000041000", "ENSMUSG00000048878", 
"ENSMUSG00000037236", "ENSMUSG00000096020", "ENSMUSG00000017002", 
"ENSMUSG00000057982", "ENSMUSG00000075370", "ENSMUSG00000037447", 
"ENSMUSG00000025139", "ENSMUSG00000039005", "ENSMUSG00000021457", 
"ENSMUSG00000073418", "ENSMUSG00000004709", "ENSMUSG00000001366", 
"ENSMUSG00000004707", "ENSMUSG00000048806", "ENSMUSG00000049410", 
"ENSMUSG00000096011", "ENSMUSG00000095270", "ENSMUSG00000095896", 
"ENSMUSG00000073811", "ENSMUSG00000000409", "ENSMUSG00000017707", 
"ENSMUSG00000038745", "ENSMUSG00000038357", "ENSMUSG00000063376", 
"ENSMUSG00000078355", "ENSMUSG00000100079", "ENSMUSG00000042228", 
"ENSMUSG00000094618", "ENSMUSG00000100505", "ENSMUSG00000094271", 
"ENSMUSG00000096582", "ENSMUSG00000096591", "ENSMUSG00000047139", 
"ENSMUSG00000037379", "ENSMUSG00000050199", "ENSMUSG00000096854", 
"ENSMUSG00000099545", "ENSMUSG00000100234", "ENSMUSG00000101163", 
"ENSMUSG00000031639", "ENSMUSG00000099518", "ENSMUSG00000099420", 
"ENSMUSG00000095101", "ENSMUSG00000094648", "ENSMUSG00000070908", 
"ENSMUSG00000100549", "ENSMUSG00000070904", "ENSMUSG00000032690", 
"ENSMUSG00000002688", "ENSMUSG00000032661", "ENSMUSG00000015316", 
"ENSMUSG00000026390", "ENSMUSG00000045364", "ENSMUSG00000009281", 
"ENSMUSG00000030148", "ENSMUSG00000051682", "ENSMUSG00000052776", 
"ENSMUSG00000015314", "ENSMUSG00000026938", "ENSMUSG00000050132", 
"ENSMUSG00000038160", "ENSMUSG00000034317", "ENSMUSG00000023349", 
"ENSMUSG00000029798", "ENSMUSG00000041135", "ENSMUSG00000046879", 
"ENSMUSG00000038151", "ENSMUSG00000026874", "ENSMUSG00000030144", 
"ENSMUSG00000030142", "ENSMUSG00000056130", "ENSMUSG00000023992", 
"ENSMUSG00000044701", "ENSMUSG00000019779", "ENSMUSG00000033307", 
"ENSMUSG00000059456", "ENSMUSG00000038527", "ENSMUSG00000025498", 
"ENSMUSG00000055172", "ENSMUSG00000098470", "ENSMUSG00000079343", 
"ENSMUSG00000038628", "ENSMUSG00000027514", "ENSMUSG00000022043", 
"ENSMUSG00000025383", "ENSMUSG00000024371", "ENSMUSG00000090231", 
"ENSMUSG00000025330", "ENSMUSG00000034889", "ENSMUSG00000028979", 
"ENSMUSG00000030114", "ENSMUSG00000021125", "ENSMUSG00000038751", 
"ENSMUSG00000079293", "ENSMUSG00000020707", "ENSMUSG00000054892", 
"ENSMUSG00000030149", "ENSMUSG00000020823", "ENSMUSG00000025532", 
"ENSMUSG00000032899", "ENSMUSG00000024349", "ENSMUSG00000032905", 
"ENSMUSG00000090485", "ENSMUSG00000054072", "ENSMUSG00000022476", 
"ENSMUSG00000024404", "ENSMUSG00000051439", "ENSMUSG00000096621", 
"ENSMUSG00000024863", "ENSMUSG00000024045", "ENSMUSG00000026187", 
"ENSMUSG00000025225", "ENSMUSG00000022887", "ENSMUSG00000025034", 
"ENSMUSG00000056851", "ENSMUSG00000056612", "ENSMUSG00000022575", 
"ENSMUSG00000000127", "ENSMUSG00000042351", "ENSMUSG00000039193", 
"ENSMUSG00000106850")), .Names = c("mgi_symbol", "external_gene_name", 
"ensembl_gene_id"), class = "data.frame", row.names = c(NA, -527L
))
```

```{r}
results_interferon_response_immune
```

## Run PCA and t-SNE for the young vs old SmartSeq2 data

```{r}
seurat_sms2 <- FindVariableGenes(object = seurat_sms2, mean.function = ExpMean, dispersion.function = LogVMR, 
    y.cutoff = 0.75)
```

```{r}
genes.var <- apply(X = seurat_sms2@raw.data , MARGIN = 1 , FUN = var)
genes.var.top <- names( sort(genes.var , decreasing = TRUE)[1:2000] )

seurat_sms2@var.genes <- genes.var.top
```

```{r}
seurat_sms2@imputed <- as.data.frame.matrix(seurat_sms2@data)
```


```{r}
seurat_sms2 <- RunPCA(object = seurat_sms2, pc.genes = seurat_sms2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5 , use.imputed = TRUE )
seurat_sms2 <- ProjectPCA(object = seurat_sms2 )
seurat_sms2 <- JackStraw(object = seurat_sms2  )
```

```{r}
PCElbowPlot(object = seurat_sms2)
```

```{r}
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type")
```


```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2)
```

```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 3)
```

```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 2, dim.2 = 3)
```


```{r}
seurat_sms2 <- RunTSNE(seurat_sms2, dims.use = c(1,2,3) , do.fast = T , seed.use = 1)
```


```{r}
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type")
```

```{r}
TSNEPlot(object = seurat_sms2)
```

```{r}
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "age")
```

```{r}
TSNEPlot(object = seurat_sms2)
```


# PCA plot PC1 vs PC2 with color

```{r}
seurat_sms2@meta.data$type_long <- seurat_sms2@meta.data$type

seurat_sms2@meta.data$type_long <- recode_factor( seurat_sms2@meta.data$type_long , q1 = "qNSC1", q2 = "qNSC2", a1 = "aNSC1" , a2 = "aNSC2" )
```

```{r}
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type_long")
```

```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2 , cols.use = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , pt.shape = "age")
```

```{r}
gg_pca <- PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2 , cols.use = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , pt.shape = "age", do.return = TRUE)
```


```{r}
gg_pca_2 <- ggplot(data = gg_pca$data , aes(x = x , y = y , color = ident , shape = pt.shape)) + geom_point() + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_color_manual(values = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , limits = c("qNSC1","qNSC2","aNSC1","aNSC2") , "Activation state") + scale_shape_manual(values = c(old = 17 , young = 16) , "Age") + coord_equal() + xlab("PC1") + ylab("PC2")

print(gg_pca_2)
```
```{r}
sd <-  GetDimReduction(object = seurat_sms2 , reduction.type = "pca" , slot = "sdev") 
pc_percentage_of_variance <- round( (sd^2/sum(sd^2))*100 , digits = 1 )

gg_pca_hollowcircle <- ggplot(data = gg_pca$data , aes(x = x , y = y , color = ident , shape = pt.shape)) + geom_point() + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_color_manual(values = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , limits = c("qNSC1","qNSC2","aNSC1","aNSC2") , "Activation state") + scale_shape_manual(values = c(old = 17 , young = 1) , "Age") + coord_equal() + xlab( paste("PC1 (",pc_percentage_of_variance[1],"%)") ) + ylab( paste("PC2 (",pc_percentage_of_variance[2],"%)" )) + coord_equal()

print(gg_pca_hollowcircle)
```



```{r fig.width=6}

gg_pca_3 <- ggplot(data = gg_pca$data , aes(x = x , y = y , fill = factor(ident, levels = c("qNSC1","qNSC2","aNSC1","aNSC2")) , shape = pt.shape) ) + geom_point(size = 2, color = "black" )  + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_fill_manual(values = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , limits = c("qNSC1","qNSC2","aNSC1","aNSC2") , "Activation state") + scale_shape_manual(values = c(old = 24 , young = 21) , "Age") + coord_equal()  + xlab( paste("PC1 (",pc_percentage_of_variance[1],"%)") ) + ylab( paste("PC2 (",pc_percentage_of_variance[2],"%)" )) + guides(fill = guide_legend(override.aes=list(shape=21)))

print(gg_pca_3)
```

```{r fig.width=6}
gg_pca_age <- ggplot(data = gg_pca$data , aes(x = x , y = y , fill = pt.shape , shape = pt.shape) ) + geom_point(size = 2, color = "black" )  + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_fill_manual(values = c( "old" =  "slateblue", "young" = "yellowgreen") , labels = c( "old" , "young" ) , name = "Age" ) + scale_shape_manual(values = c(old = 24 , young = 21) , "Age") + coord_equal()  + xlab( paste("PC1 (",pc_percentage_of_variance[1],"%)") ) + ylab( paste("PC2 (",pc_percentage_of_variance[2],"%)" )) + guides(fill = guide_legend(override.aes=list(shape=21)))

print(gg_pca_age)
```

# Make PCA plots for each subpopulation

```{r}
age_colors <- c(young = "yellowgreen" , old = "slateblue")
```


```{r}
PCAPlot_seurat <- function(object , dim1 = 1, dim2 = 2 , scale_pcs_by_sd = TRUE , pt.size = 2){

  gg_pca <- PCAPlot(object = object , dim.1 = dim1, dim.2 = dim2 , cols.use = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1") , pt.shape = "age", do.return = TRUE )
  
  sd <- GetDimReduction(object = object , reduction.type = "pca" , slot = "sdev")
  pov <- round( (sd^2/sum(sd^2))*100 , digits = 1 )
    
  if(scale_pcs_by_sd){
    gg_pca$data$x <- gg_pca$data$x * sd[[dim1]]
    gg_pca$data$y <- gg_pca$data$y * sd[[dim2]]
  }
  
  xlimits <- NULL
  ylimits <- NULL
    
  x_range <- range(gg_pca$data$x)
  x_diff <- x_range[2]-x_range[1] 
  
  y_range  <- range(gg_pca$data$y)
  y_diff <- y_range[2]-y_range[1] 

  if(x_diff > y_diff){
    
    offset <- (( x_diff - y_diff ) / 2 )
    
    y_range[1] <- y_range[1] - offset
    y_range[2] <- y_range[2] + offset

    ylimits <- y_range
        
  }else if( y_diff > x_diff ){
    
    offset <- (( y_diff - x_diff ) / 2 )
    
    x_range[1] <- x_range[1] - offset
    x_range[2] <- x_range[2] + offset
  
    xlimits <- x_range
    
  }
  
  gg <- ggplot(data = gg_pca$data , aes(x = x , y = y , fill = pt.shape , shape = pt.shape) ) + geom_point(size = pt.size, colour = "black") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank() ) + scale_fill_manual(values = age_colors , limits = names(age_colors) , name = "Age") + scale_shape_manual(values = c(old = 24 , young = 21) , limits = names(age_colors) , name = "Age") + coord_equal()  + xlab(paste("PC",dim1," (",pov[dim1],"%)")) + ylab(paste("PC",dim2," (",pov[dim2], "%)")) + ggtitle(paste0(unique(gg_pca$data$ident)) ) + guides(color = "none")
  
  if(!is.null(xlimits)){gg <- gg + xlim( xlimits )}
  if(!is.null(ylimits)){gg <- gg + ylim( ylimits )}
  
  gg
}
```


```{r}
TSNEPlot_Seurat <- function(x , title = ""){

  xlimits <- NULL
  ylimits <- NULL
    
  x_range <- range(x$tSNE_1)
  x_diff <- x_range[2]-x_range[1] 
  
  y_range  <- range(x$tSNE_2)
  y_diff <- y_range[2]-y_range[1] 

  if(x_diff > y_diff){
    
    offset <- (( x_diff - y_diff ) / 2 )
    
    y_range[1] <- y_range[1] - offset
    y_range[2] <- y_range[2] + offset

    ylimits <- y_range
        
  }else if( y_diff > x_diff ){
    
    offset <- (( y_diff - x_diff ) / 2 )
    
    x_range[1] <- x_range[1] - offset
    x_range[2] <- x_range[2] + offset
  
    xlimits <- x_range
  }
    
  gg <- ggplot(data = x , mapping = aes(x = tSNE_1 , y = tSNE_2 , fill = age  , shape = age)) + geom_point(size = 2, colour = "black") + scale_fill_manual(values = c( "old" =  "slateblue", "young" = "yellowgreen") , labels = c( "old" , "young" ) , name = "Age" ) + labs( x = "tSNE 1" , y = "tSNE 2" ) + coord_equal() + guides(color = "none") + scale_shape_manual(values = c( "old" = 24 , "young" = 21) , labels = c( "old" , "young" ) , name = "Age") + ggtitle( title )
  
  if(!is.null(xlimits)){gg <- gg + xlim( xlimits )}
  if(!is.null(ylimits)){gg <- gg + ylim( ylimits )}
  
  gg
}
```

## qNSC1

```{r}
seurat_sms2_qNSC1 <- SubsetData(object = seurat_sms2 , ident.use = "qNSC1")
seurat_sms2_qNSC1 <- FindVariableGenes(object = seurat_sms2_qNSC1 , mean.function = ExpMean, dispersion.function = LogVMR)
seurat_sms2_qNSC1 <- RunPCA(object = seurat_sms2_qNSC1 , do.print = FALSE )

pca_q1 <- PCAPlot_seurat( object = seurat_sms2_qNSC1 , dim1 = 1, dim2 = 2 )

pca_q1

PCAPlot_seurat( object = seurat_sms2_qNSC1 , dim1 = 2, dim2 = 3 )

PC_top50genes_qNSC1<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) ,  FUN=function(x){PCTopGenes(object = seurat_sms2_qNSC1 , pc.use = x , num.genes = 50 )} ) )


PCElbowPlot(object = seurat_sms2_qNSC1)

seurat_sms2_qNSC1 <- RunTSNE(object = seurat_sms2_qNSC1 , dims.use = 1:4 , seed.use = 1 )

TSNEPlot(object = seurat_sms2_qNSC1 ) 


x <- FetchData( object =  seurat_sms2_qNSC1 , vars.all = c("tSNE_1","tSNE_2","age") )

gg_tsne_qNSC1 <- TSNEPlot_Seurat(x = x , title = "qNSC1") 

gg_tsne_qNSC1
```

## qNSC2

```{r}
seurat_sms2_qNSC2 <- SubsetData(object = seurat_sms2 , ident.use = "qNSC2")
seurat_sms2_qNSC2 <- FindVariableGenes(object = seurat_sms2_qNSC2 , mean.function = ExpMean, dispersion.function = LogVMR)
seurat_sms2_qNSC2 <- RunPCA(object = seurat_sms2_qNSC2 , do.print = FALSE)

pca_q2 <- PCAPlot_seurat( object = seurat_sms2_qNSC2 , dim1 = 1, dim2 = 2 )

pca_q2

PCAPlot_seurat( object = seurat_sms2_qNSC2 , dim1 = 2, dim2 = 3 )

PC_top50genes_qNSC2<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) ,  FUN=function(x){PCTopGenes(object = seurat_sms2_qNSC2 , pc.use = x , num.genes = 50 )} ) )



PCElbowPlot(object = seurat_sms2_qNSC2)

seurat_sms2_qNSC2 <- RunTSNE(object = seurat_sms2_qNSC2 , dims.use = 1:5 , seed.use = 1 , perplexity = 13 )

TSNEPlot(object = seurat_sms2_qNSC2 ) 


x <- FetchData( object =  seurat_sms2_qNSC2 , vars.all = c("tSNE_1","tSNE_2","age") )

gg_tsne_qNSC2 <- TSNEPlot_Seurat(x = x , title = "qNSC2 (perplexity = 13)") 

gg_tsne_qNSC2
```

## aNSC1

```{r}
seurat_sms2_aNSC1 <- SubsetData(object = seurat_sms2 , ident.use = "aNSC1")
seurat_sms2_aNSC1 <- FindVariableGenes(object = seurat_sms2_aNSC1 , mean.function = ExpMean, dispersion.function = LogVMR)
seurat_sms2_aNSC1 <- RunPCA(object = seurat_sms2_aNSC1 , do.print = FALSE)

pca_a1 <- PCAPlot_seurat( object = seurat_sms2_aNSC1 , dim1 = 1, dim2 = 2 )

pca_a1

PCAPlot_seurat( object = seurat_sms2_aNSC1 , dim1 = 2, dim2 = 3 )

PC_top50genes_aNSC1<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) ,  FUN=function(x){PCTopGenes(object = seurat_sms2_aNSC1 , pc.use = x , num.genes = 50 )} ) )


PCElbowPlot(object = seurat_sms2_aNSC1)

seurat_sms2_aNSC1 <- RunTSNE(object = seurat_sms2_aNSC1 , dims.use = 1:5 , seed.use = 1 , perplexity = 7 )

TSNEPlot(object = seurat_sms2_aNSC1 ) 


x <- FetchData( object =  seurat_sms2_aNSC1 , vars.all = c("tSNE_1","tSNE_2","age") )

gg_tsne_aNSC1 <- TSNEPlot_Seurat(x = x , title = "aNSC1 (perplexity = 7)") 

gg_tsne_aNSC1
```

## aNSC2

```{r}
seurat_sms2_aNSC2 <- SubsetData(object = seurat_sms2 , ident.use = "aNSC2")
seurat_sms2_aNSC2 <- FindVariableGenes(object = seurat_sms2_aNSC2 , mean.function = ExpMean, dispersion.function = LogVMR)
seurat_sms2_aNSC2 <- RunPCA(object = seurat_sms2_aNSC2 , do.print = FALSE)

pca_a2 <- PCAPlot_seurat( object = seurat_sms2_aNSC2 , dim1 = 1, dim2 = 2 )

pca_a2

PCAPlot_seurat( object = seurat_sms2_aNSC2 , dim1 = 2, dim2 = 3 )

PC_top50genes_aNSC2<- bind_cols( lapply( list(PC1 = 1, PC2 = 2 , PC3 = 3) ,  FUN=function(x){PCTopGenes(object = seurat_sms2_aNSC2 , pc.use = x , num.genes = 50 )} ) )


PCElbowPlot(object = seurat_sms2_aNSC2)

seurat_sms2_aNSC2 <- RunTSNE(object = seurat_sms2_aNSC2 , dims.use = 1:5 , seed.use = 1 , perplexity = 7 )

TSNEPlot(object = seurat_sms2_aNSC2 ) 


x <- FetchData( object =  seurat_sms2_aNSC2 , vars.all = c("tSNE_1","tSNE_2","age") )

gg_tsne_aNSC2 <- TSNEPlot_Seurat(x = x , title = "aNSC2 (perplexity = 7)") 

gg_tsne_aNSC2
```

```{r fig.width=10, fig.height=10}
grid.arrange(pca_q1,pca_q2,pca_a1,pca_a2)
```




# t-SNE plots of subpopulations

Gather all t-SNE plots

```{r fig.height=10 , fig.width=10}
grid.arrange( gg_tsne_qNSC1 , gg_tsne_qNSC2 , gg_tsne_aNSC1 , gg_tsne_aNSC2 )
```

```{r}
saveRDS(object = seurat_sms2 , file = "seurat_sms2.RDS" )
```

# Find celltype markers

```{r}
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type_long")

celltype_markers <- FindAllMarkers(object = seurat_sms2 ,test.use = "t" , random.seed = 123 )
```

```{r}
celltype_markers
```

# Save markers of individual celltypes

```{r}
celltypes <- c("qNSC1","qNSC2","aNSC1","aNSC2")

for(i in celltypes){
  write.csv(x = celltype_markers %>% filter(cluster == i) , file = file.path("celltype_markers/",paste0("celltype_markers_",i,"_SmartSeq2.csv")) )
}
```

# GO analysis clusterCompare celltypes

```{r}
DE_results_list <- list()

for(i in celltypes){
  DE_results_list[[i]] <- celltype_markers %>% filter(cluster == i) 
}
```

## GO term analysis function

```{r fig.height=10, fig.width=20}
GO_analysis <- function(DE_results_list , p_adj_cutoff = 0.05  ){
  require("clusterProfiler")
  
  if( length(DE_results_list) < 1){
    warning("No entries in DE_results_list")
    invisible(DE_results_list)
  }
  
  de_genes_list <-list(NULL)
  for(i in seq_len(length(DE_results_list)) ){
    de_genes <- DE_results_list[[i]] %>% filter( avg_logFC > 0 ) %>% dplyr::filter( p_val_adj < p_adj_cutoff ) %>% dplyr::pull(gene)
    
    de_genes_list[[ names(DE_results_list[i]) ]] <- bitr(geneID = de_genes , fromType = "ENSEMBL" , toType = "ENTREZID" , OrgDb = "org.Mm.eg.db" )[,"ENTREZID"]
  }

    # available ontologies are:
    # BP - biological_process
    # CC - cellular_component
    # MF - molecular_function
    go_results <- compareCluster(geneClusters = de_genes_list , fun = "enrichGO" , OrgDb = "org.Mm.eg.db" , ont = "BP", pvalueCutoff = 0.05 )
      
  return(go_results)
}
```

```{r}
smartseq2_compare_GO_results <- GO_analysis(DE_results_list = DE_results_list , p_adj_cutoff = 0.01)
```

```{r fig.width=15 , fig.asp=1}
dotplot( smartseq2_compare_GO_results , showCategory = 10)
```


```{r}
smartseq2_compare_GO_results.sim <- clusterProfiler::simplify(smartseq2_compare_GO_results) 
```


```{r fig.width=15 , fig.asp=1.5}
gg2 <- clusterProfiler::dotplot(smartseq2_compare_GO_results , showCategory = 15)

gg2$data$Description <- fct_relabel( .f = gg2$data$Description , .fun = function(x){str_wrap(string = x , width = 40)} )

plotdata <- gg2$data %>% filter( p.adjust < 0.05)

levels(plotdata$Cluster) <- sub( x = levels(plotdata$Cluster) , pattern = "DE_celltype_genes_", replacement = "")

gg_clusterCompare <- ggplot(data = plotdata , mapping = aes( x = Cluster , y = Description , size = GeneRatio , color = -log10(p.adjust) ) ) + 
  geom_point() + 
  scale_color_gradient(low = "blue" , high = "red"  , breaks = c(1,10,20,30,40,50,60) , limits = c(1,60) ) + 
  theme_bw() + 
  theme( axis.text.x = element_text(colour="black",size=11), axis.text.y = element_text(colour="black",size=11, hjust = 1 ) )

gg_clusterCompare
```

```{r fig.width=10 , fig.asp=1.5}
gg_clusterCompare_center <- ggplot(data = plotdata , mapping = aes( x = Cluster , y = Description , size = GeneRatio , color = -log10(p.adjust) ) ) + 
  geom_point() + 
  scale_color_gradient(low = "blue" , high = "red"  , breaks = c(1,10,20,30,40,50,60) , limits = c(1,60) ) + 
  theme_bw() + 
  theme( axis.text.x = element_text(colour="black",size=11), axis.text.y = element_text(colour="black",size=8, hjust = 0.5 ) )

gg_clusterCompare_center
```

# Heatmap of DNA damage response genes

Load the list of DNA damage response genes.

```{r}
dna_damage_genes <- read.csv("gene_lists/DNA_damage_genes.csv", stringsAsFactors = FALSE )
```

Convert the Module column to factor.

```{r}
dna_damage_genes$module <- as.factor(dna_damage_genes$module)
```


```{r}
dna_damage_genes
```

Get the TPM gene expression values for these genes from the TPM table

```{r}
TPM_dna_damage_genes <- TPM_NSCs %>% dplyr::filter( ensembl_gene_id %in% dna_damage_genes$ensembl_gene_id  )
```

Combine the two data.frames

```{r}
TPM_combined <- left_join(x = dna_damage_genes , y = TPM_dna_damage_genes , by = "ensembl_gene_id" )
```

```{r}
head(TPM_combined)
```

```{r}
cell_order <- pseudotime_ordering %>% mutate( typesort = as.character(fct_recode(.f = type , c = "a1" , d = "a2" , a = "q1" , b = "q2") ) ) %>% group_by(age) %>% arrange(age, typesort) %>% pull(cell) %>% as.character()
```


```{r fig.width=20}
library(pheatmap)

TPM_mat <- as.matrix(TPM_combined %>% dplyr::select( c( "ensembl_gene_id" , cell_order ) ) %>% column_to_rownames("ensembl_gene_id"))

TPM_mat_names <- as.matrix(TPM_combined %>% dplyr::select( c( "gene_symbol" , cell_order ) ) %>% column_to_rownames("gene_symbol"))

anno_row <- dplyr::select(dna_damage_genes , module , ensembl_gene_id ) %>% column_to_rownames("ensembl_gene_id")


anno_col <- NSCs_annotation %>% dplyr::select(-cell)
```

##  Plot log transformed TPM values

```{r}
pheatmap(
  mat = pmin( log( TPM_mat + 1 ) , 7 ) , 
  cluster_rows = FALSE , 
  cluster_cols = FALSE , 
  scale = "none" , 
  breaks = seq(0,7,length.out = 101) , 
  color = viridisLite::viridis(n = 100) , 
  border_color = NA , 
  # gaps_row = row_breaks[seq_len(length.out = length(row_breaks) - 1 )] ,
  annotation_col = anno_col , 
  annotation_colors = list(
      type = c(q1 = "steelblue" , q2 = "steelblue1" , a1 = "tomato" , a2 = "sienna1"), 
      age = c( young = "yellowgreen" , old = "slateblue")
    )
)
```

Determine gaps between categories

```{r}
row_breaks <- cumsum( anno_row %>% group_by(module) %>% mutate( num = length(module)) %>% unique() %>% pull(num) ) 
```

Plot log transformed TPM values with gene names as rownames

```{r fig.width = 10 , fig.height=15}
anno_col_rename <- anno_col

anno_col_rename$type <- as.character(anno_col_rename$type)

anno_col_rename$type[anno_col_rename$type == "q1"] <- "qNSC1"
anno_col_rename$type[anno_col_rename$type == "q2"] <- "qNSC2"
anno_col_rename$type[anno_col_rename$type == "a1"] <- "aNSC1"
anno_col_rename$type[anno_col_rename$type == "a2"] <- "aNSC2"

anno_col_rename$type <- factor(x = anno_col_rename$type , levels = c("qNSC1","qNSC2","aNSC1","aNSC2") ) 
```

```{r fig.width = 10 , fig.height=15}
scale_white_red <- colorRampPalette(colors = c("white","red"))

pheatmap(
  mat = pmin( log( TPM_mat_names + 1 ) , 8 ) , 
  cluster_rows = FALSE , 
  cluster_cols = FALSE , 
  scale = "none" , 
  breaks = seq(0,8,length.out = 101) , 
  color = scale_white_red(n = 100) , 
  annotation_col = anno_col_rename , 
  annotation_colors = list(
      type = c(qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1"), 
      age = c( young = "yellowgreen" , old = "slateblue")
    ),
  gaps_col = 133,
  show_colnames = FALSE, 
  fontsize_row = 7
)
```


# Differentially expressed genes between old and young

## DESeq2

```{r}
library(DESeq2)
library(tibble)
library(BiocParallel)
```


Register number of cores for parallel execution
```{r}
register(BPPARAM =  MulticoreParam(workers = 4) , default = TRUE)
```


### Load the raw counts for all the cells
```{r}
countData <- read.csv(file = "count_table/raw_counts_NSCs_SMARTseq2.csv" , header = TRUE , row.names = 1)
```


And we load the cell annotation.
```{r}
colData <- NSCs_annotation

colData$type_save <- colData$type

levels(colData$type) <- c("aNSC1","aNSC2","qNSC1","qNSC2")
```

```{r}
comparisons <- list( aNSC = c("aNSC1","aNSC2") , qNSC = c("qNSC1","qNSC2") , NSC = c("qNSC1","qNSC2","aNSC1","aNSC2") , qNSC1 = c("qNSC1") ,qNSC2 = c("qNSC2"), aNSC1 = c("aNSC1"), aNSC2 = c("aNSC2") )
```


Now we want to see if all our cells available in the colData are also found in the countData table?
```{r}
all(colData$cell %in% colnames(countData))
```

And if their order is the same?
```{r}
all(colData$cell == colnames(countData)[-1])
```

Great, thus we can proceed and set up our DESeq2 objects in a list.

The comparisons we are interested in are:

- aNSC:     old vs young
- qNSC:     old vs young
- all NSCs: old vs young
- individual celltypes: old vs young

Rename the gene column
```{r}
countData <- dplyr::rename( countData ,  gene = ensembl_gene_id )
```

### Subset the data from colData and countData according to the celltypes into a list
```{r}
celltype_data_list <- lapply(
  
  X =  comparisons , 
  
  FUN = function(x){
    colD <- dplyr::filter( colData ,  type %in% x )
    
    cell_ids <- colD$cell
    
    countD <- dplyr::select( countData , one_of( "gene" , as.character(cell_ids)  ))
    
    list( colData = colD , countData = countD )
  }
)
```


#### Check the order of the celltypes in the list
```{r}
celltype_order <- lapply(
  X = celltype_data_list , 
  FUN = function(x){ as.character( unique( x$colData$type ) ) }
)

celltype_order
```


### Prepare DESeq objects
Make a list of DESeqDataSets - one for each celltype
Only keep expressed genes in the data sets
Set "young" as base level for comparison: 
  Thus a FC > 0 will mean it is higher in the old cells
  
Because we have expected reads from the RSEM output, we need to round the values to integer, as DESeq2 expects integer values

```{r makeDESEq2object}
celltype_data_list <- lapply(
  
  X = celltype_data_list , 
  
  FUN = function(x){
    rownames(x$countData) <- NULL
    rownames(x$colData) <- NULL
    
    dds <- DESeqDataSetFromMatrix(countData = round(column_to_rownames( df = x$countData , var = "gene")),
                              colData = column_to_rownames( df = x$colData , var = "cell" ),
                              design = ~ age )
    
    dds <- dds[ rowSums(counts(dds)) > 1, ]
    dds$age <- relevel(x = dds$age , ref = "young")
    
    dds
  }
)
```

### Run DESeq
Run DESeq on the list of dds objects - will be parallely run on the number of cores set in the start of this file by register() ...
```{r runDESeq2}
loopnum = 0

celltype_data_list <- lapply(
  
  X = celltype_data_list , 
  
  FUN = function(dds){
    
    loopnum <<- loopnum + 1 
    
    print(paste("Running DESeq for field:", loopnum ) )
    
    DESeq(object = dds , parallel = TRUE )
  
  }
)
```


### Extract the results from the DESeq2 data sets
```{r extractResults}
loopnum = 0

results_list <- lapply(
  
  X = celltype_data_list , 
  
  FUN = function(dds){

    loopnum <<- loopnum + 1 
    
    print(paste("Extract results for field:", loopnum ) )

    DESeq2::results(object = dds , tidy = TRUE , parallel = TRUE )  
  
  }
)
```


#### Save the DESeq2 data sets list and the results list as RDS files
```{r}
saveRDS(object = results_list , file = "results/DE_DESeq2_old_vs_young/results_list.RDS")
saveRDS(object = celltype_data_list , file = "results/DE_DESeq2_old_vs_young/DESeq2_celltype_data_list.RDS")
```


#### Extract the individual DE tables from the list and save them individually as csv files
```{r}
write.csv( x = results_list[[1]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[1]] , ".csv" ) )
write.csv( x = results_list[[2]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[2]] , ".csv"   ) )
write.csv( x = results_list[[3]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[3]] , ".csv"   ) )
write.csv( x = results_list[[4]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[4]] , ".csv"   ) )
write.csv( x = results_list[[5]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[5]] , ".csv"   ) )
write.csv( x = results_list[[6]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[6]] , ".csv"   ) )
write.csv( x = results_list[[7]] , file = paste0("results/DE_DESeq2_old_vs_young/" , "DESeq2_result_" , names(comparisons)[[7]] , ".csv"   ) )
```


## t-Test

### NSCs

```{r}
# Load the TPM expression values for the SMARTseq2 data
TPM_NSC <- remove_rownames(df = TPM_NSCs) %>% column_to_rownames(var = "ensembl_gene_id" )

# Load the cell annotation
NSC_anno <- NSCs_annotation

# Subset the expression matrix for young and old into different matrices
TPM_old <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "old",]$cell)]
TPM_young <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "young",]$cell)]

# Log transform the expression values 
TPM_old <- log(TPM_old+1)
TPM_young <- log(TPM_young+1)

TPM_NSC <- as.matrix( log(TPM_NSC+1) )

# ------------------------------------------------------------------------------------------------------------

# Calculate the mean expression, standard deviation and number of cells with 0 counts for each row (gene) for young and old cells separately

table_sms2 <- data.frame( 
  ensembl_gene_id = rownames(TPM_young),
  avg_logTPM_young = apply(TPM_young , MARGIN = 1 , FUN = mean)  , 
  avg_logTPM_old = apply(TPM_old , MARGIN = 1 , FUN = mean)  ,
  avg_logTPM = apply(TPM_NSC , MARGIN = 1 , FUN = mean) ,
  sd_logTPM_young = apply(TPM_young , MARGIN = 1 , FUN = sd)  ,
  sd_logTPM_old = apply(TPM_old , MARGIN = 1 , FUN = sd),
  fraction_cells_zero_young = apply(TPM_young == 0 , MARGIN = 1 , FUN = sum)/length(TPM_young),
  fraction_cells_zero_old = apply(TPM_old == 0 , MARGIN = 1 , FUN = sum)/length(TPM_old)
)

# Calculate the difference between the mean from old and young (old - young)
table_sms2$difference_of_avg <- (table_sms2$avg_logTPM_old - table_sms2$avg_logTPM_young )

# Calculate the mean Standard Deviation between young and old
table_sms2$mean_sd <- apply( X =  table_sms2[,c("sd_logTPM_young","sd_logTPM_old")] , MARGIN = 1 , FUN = mean)

# Finally calculate Cohens D, by deviding the difference of the mean values by the mean standard deviation 
table_sms2$'difference_of_avg/mean_sd'  <- table_sms2$difference_of_avg/table_sms2$mean_sd

# ------------------------------------------------------------------------------------------------------------
# Add p-values from t-test

# Run t-test on SMARTseq2 NSC data old and young
library(genefilter)

# Prepare factor with young and old in order of the column names
fact <- as.character( colnames(TPM_NSC) )
fact[ grepl(x = fact , pattern = "s_dot") ] <- "old"
fact[ grepl(x = fact , pattern = "^N" ) ] <- "young"
fact <- factor( fact )


# Prepare matrix for t-test input
TPM_NSC_mat <- TPM_NSC
TPM_NSC_mat <- as.matrix( TPM_NSC_mat ) 

# Run t-test with rowttests function from genefilter packages
t_test_NSCs_SMARTseq2 <- rowttests(x = TPM_NSC_mat , fac = fact )

# Add ensembl_gene_id as column from the rownames
t_test_NSCs_SMARTseq2$ensembl_gene_id <- rownames(t_test_NSCs_SMARTseq2) 

# Join the tables
table_sms2_merged_with_ttest <- full_join(x = table_sms2 , y = t_test_NSCs_SMARTseq2 , by = "ensembl_gene_id" )

table_sms2_merged_with_ttest <- arrange(table_sms2_merged_with_ttest , desc(difference_of_avg/mean_sd))

## Save the results as a csv file
# write.csv(x = table_sms2_merged_with_ttest , file = "DE_results/DE_NSC_SmartSeq2/DE_NSCs_SmartSeq2_t-test_CohensD.csv" )
```

#### Expression of differentially expressed genes from Bulk NSCs

```{r}
# Load bulk NSCs DE genes between old and young animals
bulk_DE_results <- read.csv("../Bulk_sequencing/DESeq2_results_old_vs_young/GP_DESeq2_results_Old_vs_Yang.csv" , row.names = 1)

# Merge the table of calculated values with the DESeq2 output of the bulk NSC comparison old vs young by ensembl_gene_id
tables_sms2_merged <- full_join(x = table_sms2 , y = bulk_DE_results)

# ------------------------------------------------------------------------------------------------------------

# Plot the difference in mean expression versus the parallel maximum of zero-count cell percentage
tables_sms2_merged %>% 
  filter(padj < 0.05) %>%
  ggplot( aes(
    x = pmax( fraction_cells_zero_old , fraction_cells_zero_young )  , 
    y = avg_logTPM_old - avg_logTPM_young , 
    color = factor(sign(log2FoldChange))
    )) + geom_point()

# Same plot with blue (up) and red (down) colored points and axes labels
library(ggrepel)
library(cowplot)

as_tibble(tables_sms2_merged) %>%
    filter( padj < 0.05) %>%
    mutate( col = case_when(
      padj > 0.05 ~ "n.s." , 
      log2FoldChange > 0.3 ~ "up",
      log2FoldChange < -0.3 ~ "down",
      TRUE ~ "n.s." ) ) %>% 
    ggplot( aes(
      x = pmax( fraction_cells_zero_old , fraction_cells_zero_young )  , 
      y = avg_logTPM_old - avg_logTPM_young , 
      color = col 
    )) + 
    geom_point() +
    geom_point(size = 1) +
    geom_hline(yintercept = 0 , color = "grey40") +
    xlab("Max. percentage of cells with no reads per gene in old or young") +
    ylab("Difference of avg. log(TPM+1) \n between old and young") +
    theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) +
    scale_color_manual( name = "Direction of change in bulk" , values = c(down = "red" , up = "blue")) 
    
# --> exported to PDF 6x9 inches and 4x9 (wide)


```


### individual subpopulations

```{r}
# Load the TPM expression values for the SMARTseq2 data
TPM_NSC <- remove_rownames(df = TPM_NSCs) %>% column_to_rownames( var = "ensembl_gene_id" )

# Load the cell annotation
NSC_anno <- NSCs_annotation 
NSC_anno$type <- recode_factor( NSC_anno$type , q1 = "qNSC1", q2 = "qNSC2", a1 = "aNSC1" , a2 = "aNSC2" )

# Subset the expression matrix for young and old into different matrices
TPM_old <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "old",]$cell)]
TPM_young <- TPM_NSC[as.character(NSC_anno[NSC_anno$age == "young",]$cell)]

# Log transform the expression values 
TPM_old <- log(TPM_old+1)
TPM_young <- log(TPM_young+1)

TPM_NSC <- as.matrix( log(TPM_NSC+1) )


NSC_anno_young <- NSC_anno[NSC_anno$age == "young",]
NSC_anno_old <- NSC_anno[NSC_anno$age == "old",]

# ------------------------------------------------------------------------------------------------------------

# Function that perform calculation of 
run_ttest_and_calculate_Cohens_d <- function(subpop){

    ## Subset cells for individual subpopulations
    TPM_young_subpop <- TPM_young[as.character(NSC_anno_young[NSC_anno_young$type == subpop,]$cell)]
    TPM_old_subpop <- TPM_old[as.character(NSC_anno_old[NSC_anno_old$type == subpop,]$cell)]
    TPM_NSC_subpop <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == subpop,]$cell)]
  
    # Calculate the mean expression, standard deviation and number of cells with 0 counts for each row (gene) for young and old cells separately
    table_sms2 <- data.frame( 
      ensembl_gene_id = rownames(TPM_young_subpop),
      avg_logTPM_young = apply(TPM_young_subpop , MARGIN = 1 , FUN = mean)  , 
      avg_logTPM_old = apply(TPM_old_subpop , MARGIN = 1 , FUN = mean)  ,
      sd_logTPM_young = apply(TPM_young_subpop , MARGIN = 1 , FUN = sd)  ,
      sd_logTPM_old = apply(TPM_old_subpop , MARGIN = 1 , FUN = sd),
      fraction_cells_zero_young = apply(TPM_young_subpop == 0 , MARGIN = 1 , FUN = sum)/length(TPM_young_subpop),
      fraction_cells_zero_old = apply(TPM_old_subpop == 0 , MARGIN = 1 , FUN = sum)/length(TPM_old_subpop)
    )
    
    # Calculate the difference between the mean from old and young (old - young)
    table_sms2$difference_of_avg <- (table_sms2$avg_logTPM_old - table_sms2$avg_logTPM_young )
    
    # Calculate the mean Standard Deviation between young and old
    table_sms2$mean_sd <- apply( X =  table_sms2[,c("sd_logTPM_young","sd_logTPM_old")] , MARGIN = 1 , FUN = mean)
    
    # Finally calculate Cohens D, by deviding the difference of the mean values by the mean standard deviation 
    table_sms2$'difference_of_avg/mean_sd' <- table_sms2$difference_of_avg/table_sms2$mean_sd
    
    # ------------------------------------------------------------------------------------------------------------
    # Add p-values from t-test
    
    # Run t-test on SMARTseq2 NSC data old and young
    
    # Prepare factor with young and old in order of the column names
    fact <- as.character( colnames(TPM_NSC_subpop) )
    fact[ grepl(x = fact , pattern = "s_dot") ] <- "old"
    fact[ grepl(x = fact , pattern = "^N" ) ] <- "young"
    fact <- factor( fact )
    
    # Prepare matrix for t-test input
    TPM_NSC_mat <- TPM_NSC_subpop
    TPM_NSC_mat <- as.matrix( TPM_NSC_mat ) 
    
    # Run t-test with rowttests function from genefilter packages
    t_test_NSCs_SMARTseq2 <- rowttests(x = TPM_NSC_mat , fac = fact )
    
    # Add ensembl_gene_id as column from the rownames
    t_test_NSCs_SMARTseq2$ensembl_gene_id <- rownames(t_test_NSCs_SMARTseq2) 
    
    # Join the tables
    table_sms2_merged_with_ttest <- full_join(x = table_sms2 , y = t_test_NSCs_SMARTseq2 , by = "ensembl_gene_id" )

    table_sms2_merged_with_ttest
}

# ------------------------------------------------------------------------------------------------------------

# Run the t-test for all subpopulations individually

subpopulations <- list(qNSC1 = "qNSC1", qNSC2 = "qNSC2", aNSC1 = "aNSC1", aNSC2 = "aNSC2")

ttest_results_cohensd <- lapply(X = subpopulations , FUN = run_ttest_and_calculate_Cohens_d ) 

# Order the table by cohens d
ttest_results_cohensd <- lapply(ttest_results_cohensd, FUN = function(x){arrange(x, desc(difference_of_avg/mean_sd))} )

## Save the results as a csv file
# write.csv(x = ttest_results_cohensd[[1]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[1]],".csv") )

# write.csv(x = ttest_results_cohensd[[2]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[2]],".csv") )

# write.csv(x = ttest_results_cohensd[[3]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[3]],".csv") )

# write.csv(x = ttest_results_cohensd[[4]] , file = paste0("DE_results/DE_NSC_SmartSeq2/subpopulations/DE_SmartSeq2_t-test_CohensD_",subpopulations[[4]],".csv") )
```

#### Difference of the mean Expression vs. Mean SD Plots

##### qNSC1 vs. qNSC2 

```{r}
## t-test between qNSC1 and qNSC2 SMARTseq2
## Subset cells for individual subpopulations
TPM_NSC_qNSC1 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "qNSC1",]$cell)]
TPM_NSC_qNSC2 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "qNSC2",]$cell)]

TPM_NSC_aNSC1 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "aNSC1",]$cell)]
TPM_NSC_aNSC2 <- TPM_NSC[,as.character(NSC_anno[NSC_anno$type == "aNSC2",]$cell)]


TPM_NSC_qNSC1_qNSC2 <- as.matrix( cbind( TPM_NSC_qNSC1 , TPM_NSC_qNSC2 ) ) 

q1q2 <- c( rep( "qNSC1" , 134 ) , rep( "qNSC2" , 40 ) )
t_test_qNSC1_vs_2_SMARTseq2 <- rowttests(x = as.matrix( TPM_NSC_qNSC1_qNSC2 ) , fac = factor( q1q2 ) )

tab <- data.frame(
  ensembl_gene_id = rownames(TPM_NSC_qNSC1),
  mean_qNSC1 = rowMeans( TPM_NSC_qNSC1 ) ,
  mean_qNSC2 = rowMeans( TPM_NSC_qNSC2 ) , 
  var_qNSC1 = apply(X = TPM_NSC_qNSC1 , MARGIN = 1 , FUN = var ),
  var_qNSC2 = apply(X = TPM_NSC_qNSC2 , MARGIN = 1 , FUN = var ),
  sd_qNSC1 = apply(X = TPM_NSC_qNSC1 , MARGIN = 1 , FUN = sd ),
  sd_qNSC2 = apply(X = TPM_NSC_qNSC2 , MARGIN = 1 , FUN = sd )
)

tab$mean_var <- apply(X = tab[,c("var_qNSC1","var_qNSC2")] , MARGIN = 1 , FUN = mean )
tab$mean_sd <- apply(X = tab[,c("sd_qNSC1","sd_qNSC2")] , MARGIN = 1 , FUN = mean )
tab$difference_of_avg <- (tab$mean_qNSC1 - tab$mean_qNSC2 )
tab$'difference_of_avg/mean_sd' <- tab$difference_of_avg/tab$mean_sd

# Add ensembl_gene_id as column from the rownames
t_test_qNSC1_vs_2_SMARTseq2$ensembl_gene_id <- rownames(t_test_qNSC1_vs_2_SMARTseq2) 

# Join the tables
t_test_qNSC1_vs_2_SMARTseq2_merged_with_ttest <- full_join(x = tab , y = t_test_qNSC1_vs_2_SMARTseq2 , by = "ensembl_gene_id" )


library(cowplot)

ggplot(data = t_test_qNSC1_vs_2_SMARTseq2_merged_with_ttest , aes(y = mean_sd , x = mean_qNSC1 - mean_qNSC2 , color = abs(difference_of_avg/mean_sd) > 0.8 )) + geom_point( alpha = 0.5 , size = 1    ) + ggtitle("Comparison: qNSC1 vs qNSC2") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) + xlab("Difference of mean expressions") + ylab("Average SD")

ggplot(data = tab , aes(y = mean_sd , x = mean_qNSC1 - mean_qNSC2 )) + geom_point( alpha = 0.1 , size = 1 ) + ggtitle("Comparison: qNSC1 vs qNSC2") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80")) + xlab("Difference of mean expressions") + ylab("Average SD")

```

##### qNSC1: old vs. young

```{r}
tab_q1_old_young <- run_ttest_and_calculate_Cohens_d(subpop = "qNSC1" )

ggplot(data = tab_q1_old_young , aes(y = mean_sd , x = difference_of_avg , color = abs(difference_of_avg/mean_sd) > 0.8  )) + geom_point( alpha = 0.5 , size = 1 ) + ggtitle("Comparison: qNSC1 - old vs young") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80"))  + xlab("Difference of mean expressions") + ylab("Average SD") 

ggplot(data = tab_q1_old_young , aes(y = mean_sd , x = difference_of_avg )) + geom_point( alpha = 0.1 , size = 1 ) + ggtitle("Comparison: qNSC1 - old vs young") + xlim(c(-5,5)) + theme_cowplot() + theme(panel.grid.major = element_line(colour = "grey80"))  + xlab("Difference of mean expressions") + ylab("Average SD") 
```

# Euclidean Distances (work on it)

## Load the TPM values

```{r}
smartseq2_data <- read.csv(file = "count_table/TPM_NSC_and_Astro/old_young_combined/Gene_expression_matrix_TPM_all.csv" , row.names = 1)

smartseq2_data <- smartseq2_data %>% remove_rownames() %>% column_to_rownames("gene_id")
```

Also we load the annotation

```{r}
smartseq2_annotation <- read.csv(file = "count_table/cell_annotation_NSC_Astro/old_young_combined/cell_annotation.csv" , row.names = 1)
```

Which celltypes do we have?

```{r}
celltypes <- unique(as.character(smartseq2_annotation$type))

celltypes
```

## Create Seurat object
```{r}
seurat_sms2 <- CreateSeuratObject(raw.data = smartseq2_data , min.cells = 3, project = "young_vs_old_SmartSeq2")

seurat_sms2 <- AddMetaData(object = seurat_sms2, metadata = smartseq2_annotation %>% remove_rownames() %>% column_to_rownames("cell") )

seurat_sms2 <- NormalizeData(object = seurat_sms2, normalization.method = "LogNormalize" )

seurat_sms2 <- ScaleData(object = seurat_sms2, vars.to.regress = c("nUMI", "nGene"))

seurat_sms2 <- FindVariableGenes(object = seurat_sms2, mean.function = ExpMean, dispersion.function = LogVMR, y.cutoff = 0.75)
```

```{r}
genes.var <- apply(X = seurat_sms2@raw.data , MARGIN = 1 , FUN = var)
genes.var.top <- names( sort(genes.var , decreasing = TRUE)[1:2000] )

seurat_sms2@var.genes <- genes.var.top
```

```{r}
seurat_sms2@imputed <- as.data.frame.matrix(seurat_sms2@data)
```


```{r}
seurat_sms2 <- RunPCA(object = seurat_sms2, pc.genes = seurat_sms2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5 , use.imputed = TRUE )
seurat_sms2 <- ProjectPCA(object = seurat_sms2 )
seurat_sms2 <- JackStraw(object = seurat_sms2  )
```

```{r}
PCElbowPlot(object = seurat_sms2)
```

```{r}
seurat_sms2 <- SetAllIdent(object = seurat_sms2 , id = "type")
```


```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 2)
```

```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 3)
```

```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 2, dim.2 = 3)
```

```{r}
PCAPlot(object = seurat_sms2 , dim.1 = 1, dim.2 = 4)
```

```{r}
PCAPlot(object = SetAllIdent( seurat_sms2 , id = "age" ) , dim.1 = 1, dim.2 = 4)
```


Log transform the TPM values

```{r}
smartseq2_data <- log(1+smartseq2_data)
```


Make a matrix of the data

```{r}
data_matrix_celltypes <- lapply( X = celltypes, FUN = function(x){
                        cells <- filter(smartseq2_annotation , type == x) %>% pull(cell) %>% as.character()
                        # t(as.matrix(smartseq2_data[,cells]))
                        
                        FetchData(object = seurat_sms2 , vars.all = c("PC1","PC2","PC3","PC4","PC5") , cells.use = cells )
                      })
```

Now we calculate the euclidean distance between all the cells from one celltype

```{r}
euc_dist_celltypes <- lapply( data_matrix_celltypes , FUN = function(x){as.matrix(dist(x = x, method = "euclidean" , upper = TRUE))} )
```

```{r fig.asp=0.9}
pheatmap(
  main = celltypes[[1]],
  mat = euc_dist_celltypes[[1]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9}
pheatmap(
  main = celltypes[[2]],
  mat = euc_dist_celltypes[[2]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9}
pheatmap(
  main = celltypes[[3]],
  mat = euc_dist_celltypes[[3]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9, fig.width=30}
pheatmap(
  main = celltypes[[4]],
  mat = euc_dist_celltypes[[4]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9, fig.width=10}
pheatmap(
  main = celltypes[[5]],
  mat = euc_dist_celltypes[[5]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

## Compare all samples to each other

```{r}
rownames(smartseq2_annotation) <- NULL

anno <- smartseq2_annotation %>% mutate(type = factor(type , levels = c("qNSC1","qNSC2","aNSC1","aNSC2","Astro"))) %>% arrange(age , type) %>% column_to_rownames( var = "cell")
```

```{r}
dist_all_samples <- as.matrix(dist(x = t(as.matrix(smartseq2_data)[,rownames(anno)]), method = "euclidean" , upper = TRUE))
```

```{r fig.asp=0.9 , fig.width=20}
pheatmap(
  main = "Euclidean Distance between all samples",
  mat = dist_all_samples,
  cluster_rows = FALSE, 
  cluster_cols = FALSE,
  annotation_row = anno,
  annotation_col = anno,
  annotation_colors = list( type = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1", Astro = "grey") , age = c(young = "forestgreen" , old = "red")) ,
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

## Density of euclidean distances

```{r}
df.plot.list <- lapply( X = euc_dist_celltypes, FUN = function(x){
                                            x %>% as.data.frame() %>% rownames_to_column("cell1") %>% gather(key = "cell2" , value = "euclidean_distance" , -cell1) %>% left_join( y = dplyr::rename(anno, age_cell1 = age) %>% rownames_to_column( var = "cell1") , by = "cell1" ) %>% left_join( y = dplyr::rename(anno, age_cell2 = age) %>% rownames_to_column( var = "cell2") , by = "cell2" ) %>% mutate(comparison = paste(age_cell1 , age_cell2 , sep = "_")) %>% dplyr::filter(comparison %in% c("old_old","young_old","young_young"))
})
```

```{r}
gg.list <- lapply( X = df.plot.list, FUN = function(x){
                                            ggplot(data = x, mapping = aes(x = euclidean_distance , color = comparison , fill = comparison )) + geom_density( alpha = 0.3) + ggtitle( paste0(unique(as.character(x$type.x))) )
})
```

### Astro

```{r fig.width=5,fig.height=2}
print(gg.list[[1]])
```

### aNSC1

```{r fig.width=5,fig.height=2}
print(gg.list[[2]])
```

### aNSC2

```{r fig.width=5,fig.height=2}
print(gg.list[[3]])
```

### qNSC1

```{r fig.width=5,fig.height=2}
print(gg.list[[4]])
```

### qNSC2

```{r fig.width=5,fig.height=2}
print(gg.list[[5]])
```


## Euclidean Distances between celltypes

```{r}
celltype_comparisons <- list(Astro_qNSC1 = c("Astro","qNSC1") , qNSC1_qNSC2 = c("qNSC1","qNSC2") , qNSC2_aNSC1 = c("qNSC2","aNSC1") , aNSC1_aNSC2 = c("aNSC1","aNSC2") , qNSC1_aNSC2 = c("qNSC1","aNSC2")  )

celltype_comparisons
```

Make a matrix of the data

```{r}
data_matrix_lineage <- lapply( X = celltype_comparisons, FUN = function(x){
                        cells <- filter(smartseq2_annotation , type %in% x) %>% pull(cell) %>% as.character()
                        # t(as.matrix(smartseq2_data[,cells]))
                        
                        FetchData(object = seurat_sms2 , vars.all = c("PC1","PC2","PC3","PC4","PC5") , cells.use = cells )
                      })
```

Now we calculate the euclidean distance between all the cells from the comparison

```{r}
euc_dist_lineage_all <- lapply( data_matrix_lineage , FUN = function(x){as.matrix(dist(x = x, method = "euclidean" , upper = TRUE))} )
```

```{r fig.asp=0.9, fig.width=10}
pheatmap(
  main = paste(celltype_comparisons[[1]], collapse = "_vs_"),
  mat = euc_dist_lineage_all[[1]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9, fig.width=10}
pheatmap(
  main = paste(celltype_comparisons[[2]], collapse = "_vs_"),
  mat = euc_dist_lineage_all[[2]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9, fig.width=10}
pheatmap(
  main = paste(celltype_comparisons[[3]], collapse = "_vs_"),
  mat = euc_dist_lineage_all[[3]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9, fig.width=10}
pheatmap(
  main = paste(celltype_comparisons[[4]], collapse = "_vs_"),
  mat = euc_dist_lineage_all[[4]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

```{r fig.asp=0.9, fig.width=10}
pheatmap(
  main = paste(celltype_comparisons[[5]], collapse = "_vs_"),
  mat = euc_dist_lineage_all[[5]],
  cluster_rows = FALSE, 
  cluster_cols = FALSE, 
  show_rownames = TRUE , 
  show_colnames = TRUE , 
  display_numbers = FALSE , 
  number_color = "black" , 
  fontsize = 14
)
```

### Density of euclidean distances

```{r}
df.plot.list_all <- lapply( X = euc_dist_lineage_all, FUN = function(x){
                                            x %>% as.data.frame() %>% rownames_to_column("cell1") %>% gather(key = "cell2" , value = "euclidean_distance" , -cell1) %>% left_join( y = dplyr::rename(anno, type_cell1 = type) %>% rownames_to_column( var = "cell1") , by = "cell1" ) %>% left_join( y = dplyr::rename(anno, type_cell2 = type) %>% rownames_to_column( var = "cell2") , by = "cell2" ) %>% mutate(comparison = paste(type_cell1 , type_cell2 , sep = "_"))
})
```

```{r}
gg.list_all <- lapply( X = df.plot.list_all, FUN = function(x){
                                            ggplot(data = x, mapping = aes(x = euclidean_distance , color = comparison)) + geom_density() + ggtitle( "" )
})
```

```{r}
print(gg.list_all[[1]])
```

```{r}
print(gg.list_all[[2]])
```

```{r}
print(gg.list_all[[3]])
```

```{r}
print(gg.list_all[[4]])
```

```{r}
print(gg.list_all[[5]])
```


## Euclidean distances young vs old

```{r}
q1_q2_euc_dist <- df.plot.list_all$qNSC1_qNSC2 %>% filter(comparison == "qNSC1_qNSC2")
```


```{r}
df.plot.list_ref <- lapply( X = euc_dist_celltypes, FUN = function(x){
                                            x  %>% as.data.frame() %>% rownames_to_column("cell1") %>% gather(key = "cell2" , value = "euclidean_distance" , -cell1) %>% left_join( y = dplyr::rename(anno, age_cell1 = age) %>% rownames_to_column( var = "cell1") , by = "cell1" ) %>% left_join( y = dplyr::rename(anno, age_cell2 = age) %>% rownames_to_column( var = "cell2") , by = "cell2" ) %>% mutate(comparison = paste(age_cell1 , age_cell2 , sep = "_")) %>% dplyr::mutate(type = as.character(type.x) ) %>% dplyr::select( cell1,cell2,euclidean_distance,comparison , type) %>% bind_rows(q1_q2_euc_dist %>% dplyr::mutate(type = as.character(type_cell1) ) %>% dplyr::select( cell1,cell2,euclidean_distance,comparison,type)) %>% dplyr::filter(comparison %in% c("old_old","young_old","young_young","qNSC1_qNSC2")) %>% mutate(comparison = factor(comparison , levels = c("old_old","young_old","young_young","qNSC1_qNSC2")))
})
```

```{r}
colors_comparisons <- c("old_old" = "slateblue" ,"young_old" = "deepskyblue4", "young_young" = "olivedrab" , "qNSC1_qNSC2" = "black")

gg.list <- lapply( X = df.plot.list_ref, FUN = function(x){
                                            ggplot(data = x, mapping = aes(x = euclidean_distance , color = comparison , fill = comparison )) + geom_density( alpha = 0.3) + ggtitle( paste0(unique(as.character(x$type))) ) + xlab("Euclidean Distance") + ylab("Density") + scale_fill_manual(values = colors_comparisons) + scale_color_manual( values = colors_comparisons  ) + labs(color="Comparison",fill="Comparison") + ggtitle( paste0(unique(as.character(x$type))) )
})
```

### Astro

```{r fig.width=6,fig.height=4}
print(gg.list[[1]])
```

### aNSC1

```{r fig.width=6,fig.height=4}
print(gg.list[[2]])
```

### aNSC2

```{r fig.width=6,fig.height=4}
print(gg.list[[3]])
```

### qNSC1

```{r fig.width=6,fig.height=4}
print(gg.list[[4]])
```

### qNSC2

```{r fig.width=6,fig.height=4}
print(gg.list[[5]])
```

# qNSC1 and Astrocytes

```{r}
BoxPlotSmart <- function( x , anno , idents , genes , trans_log2 = TRUE , pt.alpha = 1 , facet_switch = FALSE ){
  require(dplyr)
  require(tidyr)
  require(ggplot2)
  require(ggbeeswarm)
  require(cowplot)
  require(hash)

  ens2gene_hash <- hash( keys = genes , values = names(genes) )
  
  cells <- as.character( filter( anno , type %in% idents ) %>% pull("cell") )
  
  data_violin <- dplyr::select(x , one_of( c( "gene_id" , cells ) ) ) %>% filter( gene_id %in% genes )
  
  data_violin_gather <- gather( data = data_violin , key = "cell" , value = "TPM" , -gene_id   )
  
  if(trans_log2){ data_violin_gather$TPM <- log2( data_violin_gather$TPM + 1 ) }
  
  data_violin_gather_joined <- left_join(x = data_violin_gather , y = anno, by = "cell")
  
  print( data_violin_gather_joined  %>% group_by(gene_id,type) %>% summarise( percent = sum(TPM > 0)/length(TPM) ) , gene_id = gene_id )
  
  gene_ids <- hash::values(ens2gene_hash[genes])

  gene_ids <- gene_ids[! is.na(gene_ids)]

  genes_labeller_vec <- gene_ids

  genes_labeller <- labeller(gene_id = genes_labeller_vec)
  
  data_violin_gather_joined <- dplyr::filter(data_violin_gather_joined , gene_id %in% names(genes_labeller_vec) )
  
  g <- ggplot(data = data_violin_gather_joined , mapping = aes(x = type , y = TPM , fill = type )) + 
        geom_boxplot( outlier.shape = NA ) +    
        geom_quasirandom(alpha = pt.alpha , color = "black" , size = 0.7 ) +
        scale_x_discrete( name = "Identity") + 
        scale_y_continuous(name = if(trans_log2){"log2( TPM + 1 )"}else{"TPM"}) + 
        scale_fill_manual(name = "Identity" , values = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , Astro = "grey50" ) ) +
        if( all( c("old","young") %in% as.character(anno$age) ) ){
          if(! facet_switch){
            facet_grid(facets = gene_id~age , labeller = genes_labeller)
          }else{
            facet_grid(facets = age~gene_id , labeller = genes_labeller)    
          }      
        }else{
          facet_wrap( facets = "gene_id" , ncol = 1 , labeller = genes_labeller  )
        }
    
  
  return(g)
}
```

## Load the data

Load the SmartSeq2 data for young and old separately

```{r}
smartseq_data_old <- read.csv(file = "count_table/TPM_NSC_and_Astro/old/Gene_expression_matrix_TPM_old.csv" , header = TRUE, row.names = 1)
smartseq_data_young <- read.csv(file = "count_table/TPM_NSC_and_Astro/young/Gene_expression_matrix_TPM_young.csv" , header = TRUE, row.names = 1)
```

Join young and old

```{r}
# smartseq_data <- full_join(x = smartseq_data_old , y = smartseq_data_young)
```


and the cell annotation to celltypes

```{r}
smartseq_data_annotation_old <- read.csv(file = "count_table/cell_annotation_NSC_Astro/old/cell_annotation_old.csv" , header = TRUE, row.names = 1)
smartseq_data_annotation_young <- read.csv(file = "count_table/cell_annotation_NSC_Astro/young/cell_annotation_young.csv" , header = TRUE, row.names = 1)
```

Join young and old

```{r}
# smartseq_data_annotation <- bind_rows( smartseq_data_annotation_old, smartseq_data_annotation_young )
```

## Boxplot Cd9

```{r fig.width=4, fig.height=5}
BoxPlotSmart(x = smartseq_data_old , anno = smartseq_data_annotation_old , idents = c("qNSC1","Astro") , genes = c(Cd9 = "ENSMUSG00000030342") )

cd9plot_old <- BoxPlotSmart(x = smartseq_data_old , anno = smartseq_data_annotation_old , idents = c("qNSC1","Astro") , genes = c(Cd9 = "ENSMUSG00000030342") , pt.alpha = 1) + theme(legend.position = "none") 

cd9plot_old
```

```{r fig.width=4, fig.height=5}
cd9plot_young <- BoxPlotSmart(x = smartseq_data_young , anno = smartseq_data_annotation_young , idents = c("qNSC1","Astro") , genes = c(Cd9 = "ENSMUSG00000030342") , pt.alpha = 1) + theme(legend.position = "none") 

cd9plot_young
```

## Heatmap differentially expressed genes

Load the lists of differentially expressed genes between cortical Astrocytes and qNSC1s, for cells from old mice.

```{r}
# Old 
DEgenes_old_tab <- read.csv(file = "results/q1_old_vs_q1_oldAstro_sig_results.csv")

DEgenes_old_tab$gene_name <- str_to_title(DEgenes_old_tab$gene_name)

ens2gene_old <- DEgenes_old_tab 

DEgenes_old_up <- dplyr::filter(DEgenes_old_tab , log_fold_chang > 0 , ! is.na(gene_id))
DEgenes_old_down <- dplyr::filter(DEgenes_old_tab , log_fold_chang < 0 , ! is.na(gene_id))

seurat_10X2 <- readRDS(file = "../10X/seurat_10X2_clustered_min_1500_nGene.RDS")

DEgenes_old_up_comp <- dplyr::filter(DEgenes_old_up , gene_name %in% rownames(seurat_10X2@data) ) 
DEgenes_old_down_comp <- dplyr::filter(DEgenes_old_down , gene_name %in% rownames(seurat_10X2@data) ) 

# DEgenes_old_up_comp <- dplyr::filter(DEgenes_old_up , gene_id %in% smartseq_data_old$gene_id ) 
# DEgenes_old_down_comp <- dplyr::filter(DEgenes_old_down , gene_id %in% smartseq_data_old$gene_id )  

# DEgenes_old <- c( as.character(DEgenes_old_up$gene_name) , as.character(DEgenes_old_down$gene_name) )  
DEgenes_old_ens <- c( as.character(DEgenes_old_up$gene_id) , as.character(DEgenes_old_down$gene_id) )  

anno_old <- read.csv(file = "count_table/cell_annotation_NSC_Astro/old/cell_annotation_old.csv")

```

### Filter the SMARTseq2 TPM values for the genes that are differentially expressed

```{r}
q1cells <- filter(anno_old, type == "qNSC1") 

q2cells <- filter(anno_old, type == "qNSC2") 

astro_cells <- filter(anno_old, type == "Astro") 

up_gene_id <- as.character(DEgenes_old_up_comp$gene_id)
down_gene_id <- as.character(DEgenes_old_down_comp$gene_id)

up_gene_names <- as.character(DEgenes_old_up_comp$gene_name) 
down_gene_names <- as.character(DEgenes_old_down_comp$gene_name)

scale_red_white <- colorRampPalette(colors = c("red","white"))
```

Fetch the data from qNSC1, qNSC2 and Astrocytes for the genes

```{r}
df.heatmap.smart_up_comp <- smartseq_data_old %>% dplyr::select( one_of(c("gene_id",as.character(q2cells$cell),as.character(q1cells$cell),as.character(astro_cells$cell))) ) %>% dplyr::filter(gene_id %in% up_gene_id ) %>% arrange(gene_id)

df.heatmap.smart_down_comp <- smartseq_data_old %>% dplyr::select( one_of(c("gene_id",as.character(q2cells$cell),as.character(q1cells$cell),as.character(astro_cells$cell)))  ) %>% dplyr::filter(gene_id %in% down_gene_id ) %>% arrange(gene_id)

df.heatmap.smart_comp <- as.matrix( bind_rows( df.heatmap.smart_up_comp , df.heatmap.smart_down_comp ) %>% column_to_rownames("gene_id") )

df.heatmap.smart_comp <- log( as.matrix(df.heatmap.smart_comp) + 1 )
```

```{r}
anno_sms2 <- anno_old %>% dplyr::select(cell,type) %>% column_to_rownames("cell")
```

### Plot Heatmap

```{r fig.height=15 , fig.width=6}
hm_sms2_comp <- pheatmap(mat = pmax( pmin(df.heatmap.smart_comp,3) , -3 ) , cluster_rows = FALSE , cluster_cols = FALSE , color = rev(scale_red_white(200))  , breaks = seq(from=0,to=3,length.out = 200) , main = "log( TPM + 1 )" , show_colnames = FALSE , show_rownames = FALSE , gaps_row = 170 , gaps_col = c(17,113) , fontsize = 8 , annotation_col = anno_sms2 , annotation_colors = list( type = c(qNSC1="steelblue",qNSC2="steelblue1",Astro="grey"))  )
```

# SessionInfo


```{r}
sessionInfo()
```

